Assessing the Mechanical Properties of Cement-Based Composite Material with Partial Replacement of Cement by Sundried Cashew Nut Powder

Assessing the Mechanical Properties of Cement-Based Composite Material with Partial Replacement of Cement by Sundried Cashew Nut Powder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Mechanical Properties of Cement-Based Composite Material with Partial Replacement of Cement by Sundried Cashew Nut Powder Wasiu Olabamiji Ajagbe, Tirimisiy Adeniyi Wasiu, John Igeimokhia Braimah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6604841/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study investigates the ability of sun-dried cashew-nut powder (CNP) to serve as a renewable partial replacement for cement in concrete manufacturing. The objective of this research is to examine the mechanical properties and longevity of cement-based composites enhanced with different amounts of CNP. Specific objectives are: to define the chemical, mineralogical and physical attributes of CNP, to measure mechanical performance at replacement levels of 0%, 10%, 20%, and 30%, to establish durability indicators and to establish optimum level of CNP replacement. X-ray fluorescence (XRF), particle size distribution, and sieve analysis help define CNP. Cubic specimens with CNP at age twenty-eight and seven days are examined for, flexural resistance, resistance to compression, Stiffness (Elastic Modulus) and resistance to tensile cracking. Accelerated chloride permeability tests and water absorption tests are used to evaluate durability. The mix ratio is optimized and the relationship of CNP content, water-binder ratio, and curing time is studied with the help of response surface methodology. The study aims to ascertain a CNP replacement level that achieves the highest mechanical properties and long-term life with the lowest environmental impact of cement manufacturing. Anticipated outcomes envision CNP as a viable, sustainable cement replacement, thereby enhancing the performance and sustainability of concrete. Especially in regions where cashews are cultivated, this research invites the use of agricultural waste and offers practical guidance for green building practice in Nigeria. It also serves as the foundation for subsequent studies on long-term performance and extended application of CNP-modified concrete for infrastructure development next year. Cashew nut powder Cement replacement Sustainable concrete Mechanical properties Durability Agricultural waste valorization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1.0 INTRODUCTION “Over last few years, the global cement industry has grown fast, therefore aggravating greenhouse gas emissions and environmental damage. Global cement production has significantly risen over recent decades from less than 1 gigatons to nearly 2.5 gigatons contributing to approximately a quarter of total industrial CO₂ emissions. In regions like the Middle East and Africa, where cement production rose from 0.07 Gt to 0.26 Gt over a particular period, this growth is primarily driven by increased urbanization and infrastructure development in developing nations” (Chen et al., 2022 ). With an installed production capability of fifty-eight million and nine hundred thousand metric tonnes (MMT) per year, Nigeria is the largest cement maker in the West of Africa and is projected to have a production capacity of 53 MMT by 2040 but this growth has significant effects on public health and the environment. With about 80% of national CO₂ emissions coming from fossil fuels, cement manufacture in Nigeria mostly depends on them; exposure to particulate particles (PM2.5) from cement plants has caused major health risks mostly among children under five (Etim et al., 2021 ) contributing to 49,100 premature death in 2017 alone. While actions like enhancing energy efficiency and adopting alternative fuels has been proposed to reduce these effects, they are usually either inadequate or difficult to put into practice. (Benhelal et al., 2021 ) notes that carbon capture and storage (CCS) remain mostly at the experimental or pilot level. Thus, it's urgently required to investigate locally accessible substitutes for conventional Portland cement that are sustainable. An intriguing idea is including agricultural waste materials into concrete manufacturing. In countries like Nigeria which are rich in agriculture, such waste materials are plentiful, renewable, and inexpensive therefore they are perfect fit for sustainable construction (Madurwar et al., 2013 ). Agricultural wastes including sugarcane bagasse ash (Kilani et al., 2022 ), rice husk ash (Kumar and Satyanarayana, 2022 ), coconut shell ash (Kanjawane and Wagh 2022 ), palm oil fuel ash (He et al., 2020 ), and even coffee husks and pistachio shells (Souza et al., 2021 ; Mohammed et al., 2023 ) have been shown in earlier studies to work very well in the improvement of load bearing characteristics and performance qualities of cementitious composite and also lowering negative environmental impact. Among Nigeria's numerous agricultural resources, cashew nut production is remarkable for both its volume and economic importance. With an annual output of around 660,000 metric tonnes almost 30% of the continent's total output Nigeria is Africa's second-largest cashew nut producer (Agada and Sule, 2020 ). “About sixty percent of this national output comes from Kogi State alone. Generating between $ 253 million and $ 535 million yearly, the cashew sector is also economically quite important. It supports over 50,000 farmers and more than 55,000 people in processing and distribution” (Eze et al., 2022 ). However, significant garbage is produced by cashew nut processing, especially cashew seed shells and powders, which is typically thrown away or burned, causing more environmental load. With early results showing enhancements in concrete's compressive strength and durability and also a decrease in its total carbon footprint (Priyadharshini et al., 2021 ), recent studies suggest that this cashew nut waste can be processed into a very fine powder and utilized as a viable alternative for cement. This goes along the ideas of a very rounded economy, therefore encouraging resource efficiency and waste valorization (Ada et al., 2023 ). Despite these promising developments, comprehensive studies on the effects of cashew nut powder on concrete performance, especially within the Nigerian context, remain limited. Critical aspects such as optimal replacement ratios, long-term performance, and standardized durability metrics have not been adequately addressed. This paper aims to close the prevailing knowledge deficit by taking a critical look at the load bearing characteristics, performance under load and longevity of cementitious composite that incorporate partial cement substitution with sundried cashew nut powder (CNP), considering the pressing need to adopt more environmentally friendly construction practices and the economic importance of cashew cultivation in Nigeria. The study intends to define the physical, chemical, and mineralogy features of CNP, assess its effect on flexural resistance, resistance to compression, Stiffness (Elastic Modulus) and resistance to tensile cracking at seven and twenty-eight days while using CNP as a viable alternative cement substitute in varying proportions from 0–30% in 10% increments, analyze durability through water absorption, chloride permeability, and sulphate resistance, and identify the optimal CNP substitution level that reaches an equilibrium state between performance and sustainability. In this way, the research aids Nigeria's broader goals for sustainable development, waste management, and climate resilience in construction by promoting the production of affordable, eco-friendly building materials. 2.0 RESEARCH METHOLOGY This section details the extensive research methods used to critically investigate the impact of Cashew Nut Powder (CNP) as a viable cement replacement. The research intends to evaluate the load bearing characteristics, durability traits, and workability of CNP-altered concrete, with the primary aim of refining the mix design for enhanced performance. The approach includes material characterization, mix design formulation, specimen preparation, and a variety of tests on both fresh and cured concrete. 2.1 Materials 2.1.1 Cement A primary binder that was used in the research was Standard Portland cement (OPC) conforming to ASTM C150 (2021) and BS EN 197-1 ( 2011 ). To avoid moisture absorption and deterioration, the supplier at Agbowo area, Ibadan sourced the cement and stored it in air-tight containers in a dry, cold location. 2.1.2 Sundried Cashew Nut Powder (CNP) Cashew nut shells were collected from three local cashew processing factories in Ejigbo and Ede of Osun State. This diverse sourcing aimed to ensure a representative sample of the region's cashew waste. The collected nut shells underwent a rigorous cleaning process. Initially, they were sorted by hand to remove any foreign substances, and subsequently put in clean water so as to wash it and eliminate any traces of dirt and dust. The cleaned shells were subsequently laid out on plastic sheets and dried in the sun for 28 days, with periodic turning to guarantee even drying. The moisture levels were checked each day with a portable moisture meter until they stabilized under 5%. The shells that were dried were subsequently crushed into a powder like substance by using a pestle and mortar and a milling machine. The resulting powder was sieved through a 75 µm (No. 200) ASTM standard sieve using a mechanical sieve shaker. The fraction passing through the sieve was collected and stored in airtight containers for use in the study. 2.1.3 Fine Aggregate Fine aggregate that was used was river sand in line the specification to ASTM C33/C33M (2018). The material was obtained from a neighborhood supplier located near to the University of Ibadan. The sand was dispersed on a clean concrete platform and air-dried for 48 hours to achieve a saturated surface-dry (SSD) condition. After that, potable water was used to completely clean it and get rid of organic debris, mud, and clay as well as pollutants. Using sieves ranging from 4.75 mm to 0.075 mm, the particle size distribution was calculated according to ASTM C136/C136M (2019). 2.1.4 Coarse Aggregate The granite stone was broken down to a diameter in which the maximum was twenty millimeters. An Oyo State, Ibadan local supplier provided the granite. The stones were air-dried to a (SSD) condition then cleaned to remove pollutants and dust. Using sieves ranging from 25 mm to 4.75 mm, ASTM C136/C136M (2019) evaluated the coarse aggregate's particle size distribution. 2.1.5 Water Combining the aggregates, CNP and cement and curing the cementitious composite samples was done using potable water from the UI (University of Ibadan) water system, conforming with ASTM C1602/C1602M (2018). To make sure the standard was met, the water was tested for pH, chloride content, and total dissolved solids. 2.2 Method 2.2.1 Mix Design The cementitious composite mixes were designed in line with ACI 211.1–91 ( 2002 ) guidelines. A control mix using 100% OPC targeted a twenty-eight-day resistance to compression strength of 40 MPa. To assess the effect of Cashew Nut Powder (CNP), the water-to-binder ratio was maintained at 0.40 across all the mixes. CNP replaced cement at 0%, 10%, 20%, and 30% by weight, with an additional 15% replacement mix included for flexural and tensile strength evaluation. Mix proportions, presented in Table 1 , were adjusted as needed to maintain uniform workability, verified by slump testing in accordance with ASTM C143/C143M (2020). Table 1 Concrete Mix Proportions (kg/m³) Mix ID Cement CNP Water Fine Aggregate Coarse Aggregate CM 450 0 180 750 1020 CNP10 405 45 180 745 1015 CNP15 382.5 67.5 180 742 1012 CNP20 360 90 180 740 1010 CNP30 315 135 180 735 1005 2.2.2 Mixing procedure Following a set mixing sequence to guarantee batch consistency, concrete was made using a 50-liter capacity lab drum mixer. Initially, the aggregates were dry-mixed for one minute. Half of the entire mixing water was added next, and blending went on for another minute. Then the cement with Cashew Nut Powder (CNP) when appropriate was added and the mixture was two minutes of stirring. The remaining water was then added to correct the workability, then two minutes of mixing continued. Two minutes' waiting for the combination started the hydrating process; a final two-minute mixing cycle was then carried out. To lessen environmental variance, all mixing was carried out at a regulated room temperature of roughly 25 ± 2°C. Each batch's total mixing cycle lasted around 8 minutes. 2.2.3 Casting and curing The slump test was used to determine the workability of the cementitious composite mixture straight after mixing in accordance with ASTM C143/C143M (2020). The newly mixed material was cast into pre-lubricated steel or wooden molds according on the exact test requirements. To get rid of trapped air, each layer was compacted for fifteen seconds using a vibrating table running at 50 Hz. A steel trowel leveled and smoothed the specimens' top surface. Following molding, plastic sheets were used to cover the molds, which were then kept at a regulated ambient temperature of 25 ± 2°C for 24 hours. After being demolded, the specimens were put in a water-curing tank kept at 23 ± 2°C, in line with ASTM C192/C192M (2019). To preserve consistency and cleanliness, the curing water was changed once weekly. 2.2.4 Sample sizes and replicates In order to ensure accurate and meaningful results, multiple specimens were prepared for each test and Cashew Nut Powder (CNP) replacement level. A total of 60 cubes were cast for the resistance to compression strength tests for 15 per replacement level (0%, 10%, 20%, 30%), with three tested at seven days and three at twenty-eight days for each level. Flexural strength was evaluated using prisms, with six beams produced three for the control and three for 15% CNP replacement. For the resistance to tensile cracking test, six cylindrical specimens were cast, divided equally between 0% and 15% CNP content. Durability assessments, including Rapid Chloride Permeability Test (RCPT) and water absorption, involved 12 specimens three per replacement level. RCPT used 100 mm × 200 mm cylinders, while 100 mm cubes were used for modulus of elasticity and water absorption. Careful scheduling of casting and curing ensured all samples were available at the required testing ages, providing reliable data for robust statistical analysis of CNP’s impact on concrete performance. 2.2.5 Slump Test In line with the ASTM C143/C143M-20 the slump test was carried out to measure the workability and consistency of fresh concrete immediately after mixing. Concrete was poured into a standard slump cone in three equal layers, each was then compacted with twenty-five strokes by utilizing steel rod with a diameter of 16mm. The top was leveled, and the cone was then removed vertically within 5 ± 2 seconds. The slump was measured as the vertical distance between the displaced center of the concrete’s top surface and the top of the mould, recorded to the nearest 5 mm. The process was finished within two and a half minutes. Tests were performed for all mixes including control and CNP replacements at 10%, 20%, and 30% with three trials per mix. The final result was taken as the mean value. While the ratio of water-to-binder remained constant at 0.40, superplasticizer dosage was adjusted to maintain uniform workability across mixes. 2.2.6 Compressive Strength Test Compressive strength which is also referred to as resistance to compression tests was performed in line with ASTM C39/C39M-21 using 100 mm cubic concrete specimens. After putting the specimen in water at a temperature of about 23 ± 2°C, samples were then tested at seven and twenty-eight days so as to monitor strength development. Before testing, each specimen was surface-dried, measured and then weighed. A compressive load was applied at a controlled rate of 0.25 ± 0.05 MPa/s until failure, and strength was evaluated by dividing the peak load by the cross-sectional area. Each CNP replacement level (0%, 10%, 20%, and 30%) was assessed using three specimens per age group, with average values reported. 2.2.7 Flexural Strength Test The Flexural strength which is also referred to as the modulus of rupture and flexural resistance, was measured using the three-point bending method in line with ASTM C78/C78M-21. Prismatic were cast and cured in a manner similar to compressive strength samples. After twenty-eight days, the beams were taken from the tank used for curing, dried, and measured. Each beam was positioned on support blocks with the load applied to the cast face. Loading was applied steadily until the beam failed. The peak load was noted, and flexural strength was computed by utilizing a standard equation. Three beams were tested for each mix, and the mean value was reported. Only the control mix and the mix with 15% CNP replacement were tested for flexural strength. 2.2.8 Splitting Tensile Strength Test The splitting tensile strength also referred to as the resistance to tensile cracking test followed ASTM C496/C496M-17 using a cylindrical specimen. After twenty-eight days of curing, samples were air-dried, measured, and tested. Placed between the specimen was a thin plywood strip and the machine platens so as to evenly distribute the load. A continuous load was applied until the sample reached failure along its vertical axis. Splitting tensile strength was evaluated based on the specimen dimension and maximum load. Three specimens were tested for each mix, and average values were recorded. Only the control mix (0% CNP) and the 15% CNP mix were evaluated for this test. 2.2.9 Durability Tests Especially in hostile surroundings, concrete performance depends critically on durability. Two important durability metrics which are chloride ion penetration resistance and water absorption were the focus of this investigation. These tests aimed to see how adding Cashew Nut Powder (CNP) might affect the long-term performance and serviceability of concrete constructions. 2.2.10 Water Absorption Test Cubic samples of dimension (100mm) were created for this test, and the process adhered to ASTM C642-13. Concrete's porosity and permeability which are two important elements influencing its durability, were to be evaluated. The samples were dried in the oven at 105 ± 5°C till a constant mass was achieved after twenty-eight days of curing. Then they were put in water at 23 ± 2°C for forty-eight hours. The specimens were surface-dried with a wet towel after immersion, and their saturated weight noted. The percentage rise in weight relative to the dry mass was used to estimate the water absorption. For every CNP replacement ratios (0%, 10%, 20%, and 30%), three samples per mix were analyzed and the average water absorption recorded. 2.2.11 Data Analysis To guarantee the accuracy of findings, the data acquired from mechanical and durability tests were statistically examined. A minimum of three specimens were tested for each test parameter; means were computed together with their related standard deviations. To evaluate the variability of test findings, the coefficient of variation (COV) was established. Data visualization and regression analyses were done using Microsoft Excel 2019; statistical analyses were done by making use of IBM SPSS Statistics Version 26.0. Using Eq. 1 , the relative variations in mechanical properties were determined as percentage differences from the control mixture (0% CNP): Relative Change (%) = [(Test Value - Control Value) / Control Value] × 100 (1) To assess the temporal evolution of absorption features, the water absorption data were examined at seven and twenty-eight days for durability evaluation. Eq. 2 was used to compute the absorption shift: Absorption Change (%) = [(28-day Value − 7-day Value) / 7-day Value] × 100 (2) To assess the statistical significance of noticed variations among several CNP replacement levels, at a 95% confidence level (α = 0.05), variance analysis (ANOVA) was carried out. Factors having p-values less than 0.05 were seen as statistically relevant; the relevance of each factor was evaluated depending on its F-value and p-value. An afterward Tukey's HSD test was performed to highlight notable variances among certain replacement levels. Regression analysis was conducted to create mathematical models that illustrate the relationship between the process variables (CNP content, water-to-binder ratio, and curing duration) and the response variables (flexural strength, resistance to compression and splitting tensile strength, water absorption, and chloride penetration). The standard expression of the second-degree polynomial equation utilized for the modeling is shown in Eq. 3 : Y = β₀ + β₁₂X₁X₂ + β₂X₂ + β₁X₁ + β₁₁X₁² + β₂₂X₂² (3) In this context, Y denotes the anticipated response, β₀ is the constant term, while β₁ and β₂ are the linear terms. The coefficient β₁₂ indicates interaction effects, and β₁₁ and β₂₂ represent the quadratic terms. The variables X₁ and X₂ signify the coded values for CNP content and curing duration, respectively. The effectiveness of the created models was assessed through the coefficient of determination (R²), adjusted R², and predictive R². A lack of fit test was conducted to evaluate the appropriateness of the models, with a non-significant lack of fit (p > 0.05) suggesting a strong alignment between the model and the data. The optimization of CNP content was determined through a comprehensive evaluation of mechanical and durability parameters, considering the acceptable threshold values for structural applications. Maximum allowable strength reduction limits, durability performance indicators, and environmental advantages attained by cement reduction were all included in the assessment criteria. Using Origin Pro 2023b, graphical representations helped to accomplish results visualization. Mean values and standard deviations at various CNP replacement levels were shown using bar graphs with error bars to display the mechanical qualities. The relationship between several mechanical features and their relative variations with rising CNP content was depicted using combined bar-line graphs. 3.0 RESULTS AND DISCUSSION The results of the work carried out in this investigation are presented in this chapter together with a thorough discussion of them. To enable easy interpretation, the results of several tests and evaluations are shown in tables and graphs. The discussion section closely analyzes these findings, contrasts them with earlier research, and investigates their consequences for the utilization of (CNP) as a extra cementitious ingredient in concrete manufacture. 3.1 Sieve Analysis The Sieve analysis test which was done in a way that aligns with ASTM C136/C136M-19 revealed the particle size distribution of the Cashew Nut powder (CNP). For a 200g sample of CNP powder the data for the sieve analysis are illustrated in Table 2 and Fig. 1 Table 2 Sieve Analysis Results for CN Powder Sieve Size Weight Retained (g) % Retained Cumulative % Retained % Passing 4.75 mm 5.9 2.95 2.95 97.05 2.36 mm 37.9 18.95 21.90 78.10 1.18 mm 86.0 43.00 64.90 35.10 600 µm 67.9 33.95 98.85 1.15 425 µm 2.00 1.00 99.85 0.15 212 µm 0.1 0.05 99.90 0.10 150 µm 0 0.00 99.90 0.10 75 µm 0 0.00 99.90 0.10 Pan 0 0.00 99.90 0.10 Total 199.8 99.90 - - The sieve analysis results indicated that most of the CNS powder particle remained between the 600 µm and 2.36 mm sieves. On the 600-um sieve, 33.95% was kept; on the 1.18 mm sieve, 43.00% was retained. This suggests that the CNS powder consists mostly of medium to coarse particle sizes. Having a cumulative percentage passing through the 4.75 mm sieve of 97.05%, the material may be classified under ASTM C33 requirements as a fine aggregate. Notably, only 1.15% of the particles passed the 600 µm sieve, suggesting that the CNS powder contains few extremely fine particles. Since a greater proportion (64.90%) of the material is coarser than 1.18 mm, the particle size distribution may not match the conventional gradation requirements for concrete's fine aggregates. This property might influence how the powder will behaves in concrete mixes, therefore impacting water needs and application simplicity. By summing the cumulative percentage retained on standard sieves and dividing by 100, a fineness modulus of 3.88 was achieved. This figure, exceeding the typical range of 2.3 to 3.1 for aggregates(fine) used in concrete, highlights the coarser nature of the CNS powder. X-Ray Fluorescence (XRF) Analysis (XRF) analysis which is a non-destructive analysis. Quantitative assessment of the elemental makeup of the Cashew Nut Powder (CNP) was obtained using XRF analysis. This study provides important information on the possible behavior of CNP as a supplementary cementitious component. Using an XRF spectrometer run at 40.0 KV and 350 µA, with a 100-second test duration, the examination was carried out. Figure 2 shows the XRF findings. When compared to conventional supplementary cementitious materials (SCMs) like fly ash, ground granulated blast furnace slag (GGBS), or silica fume, Fig. 5 illustrates a distinct elemental profile for CNP.The spectrum shows a broad range of components; their relative abundances in the sample correspond to different peak intensities. The great tin (Sn) concentration of 4.6144% and antimony (Sb) 3.9040% that stand out most in the composition are Though these components are seldom found in such concentrations in concrete-compatible materials, they appear as separate peaks toward the right end of the spectrum. Their significant presence calls for close attention since it overwhelms the basic CNP profile. Such high levels of tin might have a marked influence on the qualities of concrete blends containing CNP. Known to disrupt cement hydration processes, tin compounds could slow the curing time of cement pastes and thereby influence the early strength development of concrete. CNP's effect on setting times and strength development patterns in concrete mixtures must be carefully studied because this potential for changing hydration kinetics calls for it. Likewise, the great antimony content causes worries about its effects on concrete characteristics and environmental safety. The clear peak for antimony in the spectrum highlights its remarkable abundance, which is unusual in Standard SCMs. This underscores the importance of thorough safety evaluations, especially about possible leaching behavior of antimony in concrete matrices. Among the little quantities found, potassium (K) is especially interesting at 0.6984% since a clear peak can be seen in the left part of the spectrum. Although not very high, this amount of potassium helps to determine the alkali content of the substance, which is essential in the chemistry of concrete. When estimating the overall alkali content of concrete blends to reduce possible alkali-silica reaction (ASR) hazards, the potassium concentration in CNP should be closely taken into account. The spectrum also reveals peaks for iron (Fe), copper (Cu), and zinc (Zn), which match the observed levels of 0.3140%, 0.2249%, and 0.2641% respectively. The iron content might help to determine the color of the finished concrete and would have minimal influence on cement hydration, therefore affecting early strength development via its involvement in ettringite formation. Present at 0.2463%, sulfur (S) is another element of interest in cementitious systems even if its peak is less pronounced in the spectrum. Sulfur in CNP might change the sulfate balance in concrete mixes, therefore influencing early strength development and setting times. Especially low compared to conventional pozzolanic sources are silicon (Si) levels of 0.0545% and calcium (Ca) levels of 0.0417%. Their rather modest peaks on the left side of the spectrum reflect this. These elements are essential for producing calcium silicate hydrate (C-S-H), the primary compound responsible for strength in hydrated cement paste. Their low concentrations imply that CNP could not contribute much to pozzolanic reactions, hence suggesting that its role in concrete could be more as a filler than an active pozzolanic material. Mo shows a particularly strong peak among other elements including rubidium (Rb), zirconium (Zr), niobium (Nb), and molybdenum (Mo). Although not often important in cementitious materials, these components help to define CNP uniquely and may affect how it behaves in concrete in ways needing more study. It's also noteworthy that some elements particularly magnesium (Mg) and titanium (Ti) are absent; the absence of unique peaks for these elements in the spectrum points to this. This more distinguishes CNP from other SCMs and could affect the kinds of hydration products generated in concrete mixes with CNP. CNP has several uses as a extra cementitious material given its unusual elemental makeup as clearly shown in the XRF spectrum. Limited pozzolanic action is indicated by the low amounts of silicon, aluminum, and calcium, therefore possibly implying little contribution to strength growth through pozzolanic reactions. Rather, CNP could mostly serve as a concrete filler, perhaps enhancing particle packing but not much aiding in the chemical interactions of cement hydration. 3.2 Compressive Strength Development By the twenty eight and seven days of curing, the resistance to compression evolution of CNP-modified cement-based composites was analysed. As seen in Fig. 3 , CNP content had a clear impact on strength growth. The experimental data depicted in Fig. 3 showed a remarkable drop in compressive strength in the cementitious composites as CNP level rises. Specimens with CNP showed significant strength losses, whereas the control mixture reached compressive strengths of 13.57 MPa and 20.53 MPa at seven and twenty-eight days correspondingly. At 10% replacement, the compressive strength went down to 2.79 MPa at seven days and 11.53 MPa at 28 days, or 79.5% and 43.8% respectively. With 28-day strengths dropped to 5.18 MPa and 2.37 MPa, respectively, further increases in CNP content to 20% and 30% caused much more pronounced strength loss. This marked drop in strength profile differs greatly from the performance generally seen with traditional supplementary cementitious materials (SCMs). Unlike results published in the current literature for agricultural waste-based SCMs, the experimental data shows a more pronounced effect on mechanical characteristics. For instance, (Muleya et al., 2021 ) study on rice husk ash inclusion at comparable replacement ratios (10–30%) recorded the resistance of compression of the cementitious composite decreased with increasing rice husk ash content. Specifically, the 20% replacement mix produced an optimum compressive strength of 18 MPa at a 0.5 water/binder ratio, while higher replacement levels resulted in further strength reductions. Similarly, studies by Onikeku et al ( 2019 ) looked into the effects of incorporating bamboo leaf ash (BLA) into concrete mixtures at replacement levels of 0–20% with a 5% increment. The findings indicated that the resistance to compression of the concrete decreased with increasing BLA content. Specifically, at twenty-eight days, the resistance to compression of the concrete with 10% BLA replacement was approximately 95% of the control mix, while the strengths for 15% and 20% BLA replacements were approximately 80% and 70% of the control mix, respectively. Particularly at early ages, the remarkable strength loss matches more closely results of research on materials with great metallic content. Research by Qureshi, T. ( 2015 ) observed severe strength impediment in cement composites containing industrial wastes with elevated tin concentrations, reporting strength reductions of 40–60% at 10% replacement levels. This correlation supports the hypothesis that the high tin and antimony content in CNP, as identified in the XRF analysis, may be significantly interfering with the cement hydration mechanisms. The strength development pattern observed in this study, characterized by particularly poor early-age strength followed by relatively higher strength gotten between seven and twenty eight days (especially in the 10% CNP mixture). This pattern suggests a potential retarding influence on cement hydration, possibly caused by the formation of metallic hydroxides that interfere with normal C3S and C2S hydration processes. The ANOVA results is depicted in Table 3 . Table 3 Summary of ANOVA Results for Compressive Strength (Y 1 ) Source Sum of Squares Degree of freedom Mean Square F-value p-value Significance Model 724.86 5 144.97 98.63 < 0.0001 significant X1-CNP Content 598.42 3 199.47 135.69 < 0.0001 significant X2-Age 102.53 1 102.53 69.75 < 0.0001 significant X1X2 23.91 1 23.91 16.27 0.0008 significant Residual 23.52 16 1.47 - - - Lack of Fit 12.84 4 3.21 3.02 0.0557 not significant From the ANOVA table (Table 3 ), the model is highly important, with an F-value of 98.63 and a p-value of less than 0.0001. This indicates that the model provides an excellent fit for the data and that the independent variables (CNP content and age) collectively have a very serious influence on the resistance to compression of the concrete specimens. Statistical guidelines say that a high F-value in the ANOVA table means that at least one of the regression coefficients is non-zero, therefore showing a relationship that is very strong between the predictor and response variables. With a p-value under 0.0001 and an F-value of 135.69, the CNP content (X₁) has the most significant impact. This very important finding suggests that the inclusion of CNP greatly affects the concrete's compressive strength. The high F-value implies that the CNP content mostly governs the variation in compressive strength. The age element (X₂) is also extremely important; with an F-value of 69.75 and a p-value under 0.0001, indicates the curing age has a major influence on the development of resistance to compression of concrete. The high F-value suggests that age has a critical role in strength development, which is in accordance with the basic ideas of concrete curing and strength gain over time. With an F-value of 16.27 and a p-value of 0.0008, the relationship between CNP content and age (X₁X₂) is important. This implies that the CNP level that has an impact on compressive strength differs with age, and vice versa, hence indicating a synergistic connection between these elements. This interaction effect suggests that the effect of CNP content on strength growth varies depending on the curing age. The model sufficiently reflects the relationship between the variables, as shown by the low fit (p = 0.0557). This confirms the model's accuracy in forecasting compressive strength depending on CNP content and age, therefore implying that the preferred model structure fairly depicts the underlying data relationships. The regression equation using Y 1 for compressive strength is given in Eq. ( 1 ): $$\:Y₁\:=\:14.26\:-\:0.498X₁\:+\:0.396X₂\:-\:0.009X₁X₂$$ 1 As seen in Eq. 1 , the regression model for compressive strength response is a linear model that connects the compressive strength (Y₁) to two input variables: age (X₂) and CNP content (X₁). The model includes both main effects and interaction terms to capture the connection between these variables and the compressive strength of concrete specimens. The CNP content (X₁) has a significant negative coefficient (-0.498), indicating that increasing CNP content results to a substantial drop in compressive strength. This strong negative relationship suggests that higher percentages of CNP in the concrete mixture results strength values that have reduced, which is clearly evidenced in the experimental data where strength decreased from 20.53 MPa in the control mixture to 2.37 MPa at 30% CNP content at 28 days. The age factor (X₂) has a positive coefficient (0.396), demonstrating that increasing curing age contributes to higher compressive strength. This positive relationship aligns with fundamental concrete behavior, where strength typically increases with age due to continued hydration processes and cement matrix development. The size of this coefficient suggests that age considerably enhances strength development; however, its influence differs with CNP content because of the notable interaction term. The interaction term (X₁X₂) has a negative coefficient (-0.009), indicating an antagonistic effect between CNP content and age. This significant interaction (as confirmed by the ANOVA p-value of 0.0008) suggests that the positive impact of age on the development of strength becomes less pronounced at higher CNP contents. This interaction helps explain why specimens with higher CNP content show different strength development patterns compared to the control mixture. The constant term (14.26) represents the baseline compressive strength when both variables are at their reference levels. This value approximates the expected strength for the control mixture at early age, providing a reasonable baseline for comparing the effects of CNP addition and age variation. The model accurately reflects the separate and joint influences of CNP content and age on compressive strength, where the important negative effect of CNP content is the primary cause of strength decline, influenced by age and the interplay between age and CNP content. The statistical data for the Compressive Strength fit is presented in Table 4 : Table 4 Fit Statistics for Compressive Strength (Y 1 ) Fit Statistics Value Standard Deviation (SD) 1.21 Mean 7.43 Coefficient of variance (CV%) 16.29 Adjusted R² 0.958 Coefficient of determination (R²) 0.969 Adequate Precision 28.764 Predicted R² 0.942 The Fit Statistics table (Table 4 ) provides insight into the model's performance. A standard deviation (SD) of 1.21 MPa reflects how much the compressive strength values deviate from the mean, indicating good precision given the broad strength range (1.60–20.53 MPa). The coefficient of variation (CV), calculated as the SD divided by the mean, is 16.29%, showing moderate variability reasonable considering the wide CNP content range (0–30%) and differences between early and later curing ages. The coefficient of determination (R²) of 0.969 shows that 96.9% of the variation in resistance to compression is explained by age and CNP content. This high value confirms a strong model fit. The adjusted R², which accounts for the number of predictors, is 0.958, indicating that overfitting is minimal and all predictors are relevant. A predicted R² of 0.942 further confirms the model’s ability to generalize to new data. The small gap between R², adjusted R², and predicted R² (all within 0.027) suggests the model is both stable and accurate. Lastly, an adequate precision value of 28.764, well above the acceptable threshold of 4, indicates a high signal-to-noise ratio. This shows that the model can effectively distinguish between different levels of compressive strength, making it reliable for prediction across the tested design space. 3.3 Splitting Tensile Strength Analysis The analysis of splitting tensile strength for CNP-modified cement-based composites was assessed at both seven and twenty-eight days of curing. The results revealed a significant influence of CNP content on strength development, as illustrated in Fig. 4 . The resistance to tensile cracking tests revealed significant variations between the control mixture and CNP-modified specimens across both testing ages. The control specimens (0% CNP) demonstrated strength development from 7 to 28 days, with values increasing from 2.50 MPa to 2.87 MPa. The 7-day results showed individual values ranging from 2.12 MPa to 2.88 MPa, while 28-day values ranged from 2.4 MPa to 3.3 MPa, demonstrating reasonable consistency. In contrast, the 15% CNP replacement specimens showed markedly reduced strength values at both ages, with 7-day strength averaging 0.92 MPa and increasing marginally to 1.05 MPa at twenty-eight days. The reduction in resistance to tensile cracking with 15% CNP replacement shows about a 63.2% reduction at 7 days and a 63.4% reduction at 28 days in contrast to the control mixture. This significant reduction in tensile capacity is consistent with the compressive strength trends noted earlier, although the extent of reduction seems to be very pronounced in the resistance to tensile cracking findings. The comparable reduction rates at both ages indicate that the adverse effect of CNP on tensile strength stays fairly consistent throughout the curing duration. The specimen weights provide additional insight into the material characteristics. Control specimens maintained consistent weights, showing a minimal increase from 7 to 28 days (508.25 g to 509.48 g), indicating good uniformity in the mixture proportioning and specimen preparation. The 15% CNP specimens showed significantly lower weights at both ages, averaging 299.87 g at 7 days and slightly increasing to 300.48 g at 28 days, representing approximately a 41% reduction in mass compared to the control specimens. The minimal weight changes between 7 and 28 days for both mixtures suggest stability in the hardened state properties. These findings align with prior observations (Zhang et al. 2021 ) that incorporating certain waste materials into cementitious composites often results in more significant reductions in tensile strength than in compressive strength. This effect is especially notable when such materials interfere with proper cement hydration or alter the mix composition in a way that compromises matrix integrity. The great drop in specimen weight with CNP inclusion points to notable microstructure and density variations in the material. This findings are in line with those of Nadzri et al. ( 2017 ), who observed that, because agricultural waste-based additives have lower specific density than cement, they frequently result in reduced unit weight. The degree of weight loss seen in the current study (41%) is especially large compared to usual data published in literature (usually 10–20% for comparable replacement levels), therefore CNP inclusion may be influencing the consolidation qualities of the matrix or producing more void space. The lack of results for the third sample in the 15% CNP mixture points to possible problems with specimen integrity or testing protocols, which could be related to the considerably lower strength and maybe handling sensitivity of the changed mixture. This finding shows issues about the material's readiness and strength. The ratio of resistance to tensile cracking to resistance to compression in normal concrete typically ranges from 8–14%. In this study, the control mixture showed a ratio within this expected range. However, the CNP-modified specimens showed an altered relationship between tensile and compressive strengths, suggesting fundamental changes in the material's mechanical behavior and load-carrying mechanisms. The dramatic reduction in both strength and weight indicates that the incorporation of CNP fundamentally alters the material's physical and load bearing properties. These changes could be related to several factors: the interference of metallic components with cement hydration processes, increased porosity in the matrix, reduced bond strength between paste and aggregates, and possible changes in the pore structure and distribution. These findings suggest that applications of CNP-modified concrete would be severely limited in a situation where tensile strength is a important design consideration, such as in flexural members or elements subject to splitting forces. The ANOVA results is illustrated in Table 5 . Table 5 Summary of ANOVA Results for Splitting Tensile Strength (Y 2 ) Source Sum of Squares Degree of freedom Mean Square F-value p-value Significance Model 5.876 3 1.959 42.13 < 0.0001 Significant X 1 -CNP Content 5.427 1 5.427 116.73 < 0.0001 Significant X 2 -Age 0.375 1 0.375 8.07 0.0149 Significant X 1 X 2 0.074 1 0.074 1.59 0.2311 not significant Residual 0.465 10 0.0465 - - - Lack of Fit 0.198 2 0.099 2.95 0.1027 not significant As shown in Table 5 , the ANOVA results confirm the model's strong significance, with a F-value of 42.13 and a p < 0.0001. This suggests the model aligns effectively the data well and that the independent variables CNP content and curing age significantly influence the resistance to tensile cracking. A high F-value typically indicates that at least one predictor is meaningfully related to the response. Among the variables, CNP content (X₁) has the strongest impact, demonstrated by an F-value of 116.73 and a p-value < 0.0001. This highlights the major role of CNP in altering tensile strength. The age factor (X₂), with an F-value of 8.07 and p = 0.0149, also has a statistically significant effect, though less pronounced, aligning with the expected strength gain over time. The interaction between CNP content and age (X₁X₂) is not significant (p = 0.2311), suggesting that their effects are independent rather than combined. The lack of fit has a p-value of 0.1027, indicating it is not statistically significant and that the model adequately represents the relationship between the inputs and tensile strength. The regression equation for resistance to tensile cracking(Y₂) is presented in Eq. ( 2 ). $$\:Y2\:=\:2.83\:-\:0.105X₁\:+\:0.018X₂\:-\:0.001X₁X₂$$ 2 Where: Y 2 = Splitting Tensile Strength/resistance to tensile cracking (MPa) X₁ = CNP Content (%) X₂ = Age (Days) The regression equation for splitting tensile strength is a linear model that incorporates both main effects CNP content (X₁) and curing age (X₂) as well as their interaction (X₁X₂). A negative coefficient for CNP content (-0.105) indicates that higher CNP levels reduce splitting tensile strength, consistent with experimental data indicating a decline in strength at 15% replacement compared to the control. In contrast, the positive coefficient for age (0.018) reflects the typical strength gain of concrete over time due to ongoing hydration. The interaction term has a small negative coefficient (-0.001), implying a slight weakening of age-related strength development at higher CNP levels. However, as noted earlier, this interaction is statistically insignificant (p = 0.2311), indicating that age and CNP content affect tensile strength largely independently. The intercept (2.83) represents the estimated baseline tensile strength at reference levels for both variables, closely matching early-age strength of the control mix. Fit statistics for this model are summarized in Table 6 . Table 6 Fit Statistics for Splitting Tensile Strength (Y 2 ) Fit Statistics Value Standard Deviation 0.216 Mean 1.835 Coefficient of variance (CV%) 11.77 Adjusted R² 0.905 Adequate Precision 19.842 Predicted R² 0.876 Coefficient of determination (R²) 0.927 The Fit Statistics table (Table 6 ) offers insight into the model's precision and reliability. A standard deviation of 0.216 MPa reflects low dispersion in splitting tensile strength data, indicating consistent experimental results. The coefficient of variation (CV) is 11.77%, representing moderate variability, which remains acceptable considering the influence of CNP on mix consistency. The model shows a strong fit with an R² of 0.927, meaning it accounts for 92.7% of the variability in splitting tensile strength. The adjusted R² is 0.905, suggesting minimal risk of overfitting, while the predicted R² of 0.876 confirms good model performance on unseen data. The close range among these values (within 0.051) supports both accuracy and stability. An adequate precision value of 19.842 greatly surpasses the acceptable limit of 4, indicating a robust signal-to-noise ratio and the model’s capability to distinguish between response levels effectively. Overall, these metrics validate the model’s reliability in predicting tensile strength based on CNP content and curing age. 3.4 Flexural Strength Analysis The flexural strength/ Flexural resistance assessment compared the control mixture (0% CNP) with specimens containing 15% CNP replacement. The test results revealed significant constrast in both early-age and ultimate strength development between the control and CNP-modified specimens as depicted in Fig. 5 . In comparison to the control, the 15% CNP replacement mix had much lower strengths 1.12 MPa at seven days and 1.67 MPa at twenty-eight days resulting in a somewhat lower ratio of 0.67; early and late strengths were reduced 60.7% and 59%, respectively. The control mix had a strength development ratio of 0.70 and 2.85 MPa at 7-day flexural strength, rising to 4.07 MPa at 28 days. Though flexural behavior exhibited its own unique sensitivities, CNP addition significantly influenced flexural strength, a trend also seen in tensile and compressive strength measurements. This fits with known facts since flexural strength depends mostly on tensile characteristics and the quality of the (ITZ) interfacial transition zone, which makes it more susceptible to variations in the concrete matrix. Several factors might underlie the reduced strength and load-deflection performance observed in CNP-modified samples. The lower unit weight suggests increased porosity, which typically compromises flexural performance. Similar outcomes have been reported in studies (Norambuena-Contreras et al 2018 ) involving waste-based cementitious materials, where certain component such as metallic elements might disrupt with the bond between the cement paste and aggregate, thereby weakening the overall structural integrity. This drop in flexural capacity poses questions for uses exposed to bending structurally. Deviations in the CNP mixes show basic changes in load distribution and failure behavior even if flexural strength in normal concrete is normally 10–15% of compressive strength. For components such beams or pavements, this could call for design changes such as greater reinforcement or section sizes, therefore offsetting the environmental or financial advantages of CNP usage. Furthermore, changed microstructural characteristics could compromise the crack resistance of the material, which is important in flexural performance. The ANOVA results is illustrated in Table 7 . Table 7 Summary of ANOVA Results for Flexural Strength (Y 3 ) Source Sum of Squares Degree of freedom Mean Square F-value p-value Significance Model 11.84 3 3.95 132.41 < 0.0001 significant X₁-CNP Content 9.67 1 9.67 324.28 < 0.0001 significant X₂-Age 2.03 1 2.03 68.07 < 0.0001 significant X₁X₂ 0.14 1 0.14 4.69 0.0549 not significant Residual 0.30 10 0.030 - - - Lack of Fit 0.11 2 0.055 2.29 0.1577 not significant With an F-value of 132.41 and a p-value < 0.0001, the ANOVA findings (Table 7 ) show that the model is very significant, therefore implying that curing age and CNP content together have a major influence on concrete's flexural strength. Since a high F-value implies that at least one regression coefficient is non-zero, it supports the existence of a strong bond between the predictor variables and the response. With an F-value of 324.28 and a p-value < 0.0001, CNP content (X₁) had the most impact among the predictors, hence variations in CNP dose clearly affect flexural strength. Though the impact of CNP content is more pronounced, the age factor (X₂) also exhibited a strong effect with an F-value of 68.07 and a p-value < 0.0001, therefore underlining the significance of curing time in strength development. With a p-value of 0.0549, the interaction term (X₁X₂) had an F-value of 4.69, suggesting it not formally significant. This suggests that CNP age and content have mostly independent influences on flexural strength. The low lack of fit p-value (0.1577) further strengthens the model's reliability in projecting flexural strength depending on the two variables by confirms its adequate representation of the observed data. Equation ( 3 ) gives the regression model for flexural strength (Y₃). $$\:{Y}_{3}\:=\:3.76\:-\:0.173X₁\:+\:0.047X₂\:-\:0.002X₁X₂$$ 3 As shown by the equation, the regression model for flexural strength (Y₃) is a linear model including both main effects and an interaction term to describe the relationship between flexural resistance, CNP content (X₁), and curing age (X₂). The negative coefficient for CNP content (-0.173) indicates that more flexural strength is lost the more CNP is added to the concrete mix. Experimental data clearly support this trend; for a 15% CNP substitution, they show a drop from 4.07 MPa in the control combination to 1.67 MPa at twenty-eight days indicating a significant decrease in structural performance. The usual response of concrete where strength rises with time because of continuous hydration and the gradual formation of the cementitious matrix is shown by the positive coefficient for age (0.047). Though this contribution is small relative to that of CNP material, it is still very important for strength growth. With a small negative coefficient (-0.002), the interaction term (X₁X₂) suggests CNP content and age have a slight oppositional connection. Its p-value (0.0549) indicates, though, that this interaction is not statistically significant, therefore strengthening the view that the two factors have mostly independent influences on flexural strength. When both input variables are at their reference values, the constant term (3.76) denotes the baseline flexural strength. This provides a good reference point for evaluating how different CNP amount and age affect things. Table 8 offers the flexural strength model's fit data. Table 8 Fit Statistics for Flexural Strength (Y 3 ) Fit Statistics Value Standard Deviation (SD) 0.173 Mean 2.43 Coefficient of determination (R²) 0.975 Predicted R² 0.952 Coefficient of variance (CV%) 7.12 Adjusted R² 0.968 Adequate Precision 35.842 Important information about the regression model's reliability and precision for flexural strength is found in the Fit Statistics table (Table 8 ). A (SD) of 0.173 MPa indicates how much the flexural strength values dispersed about the mean are varied. Considering the observed range of strength values (1.12 MPa to 4.07 MPa), this somewhat low value implies a high degree of accuracy in model forecasts and experimental calculations. 7.12% is the (CV), which is the ratio of the (SD) to the mean presented as a percentage. This low CV value suggests great consistency across the test conditions and indicates little relative variability in the flexural strength data. In concrete study, CV values under 10% usually indicate great quality, therefore confirming the dependability of the experimental methods and results. With a value (R²) of 0.975, the model explains 97.5% of the variation in flexural strength. With an extraordinarily high R² number, this confirms that the regression model fits well and clearly reflect the relationships between age, CNP content, and flexural strength. In materials research, a good model is usually indicated by an R² value higher than 0.9. With an 0.968 adjusted R² which corrects for the number of predictors and guards against overfitting the minimal difference of 0.007 between R² and adjusted R² suggests that the model’s predictors are all meaningful and that overfitting is not a concern. A projected R² value of 0.952 reinforces the capacity of the model to generalize and predict fresh observations. Strong model stability and predictive performance are seen in the tight agreement among R², adjusted R², and predicted R² (all within 0.023 of one other). Well above the threshold of 4 usually regarded as acceptable, adequate precision measuring the signal-to-noise ratio stands at 35.842. This very high value suggests that the model is quite reliable for design optimization and predictive uses depending on variations in CNP content and curing age as well as can successfully discriminate between several degrees of response. 3.5 Modulus of Elasticity Analysis The elastic modulus of concrete, a fundamental property governing structural deformation behavior, was evaluated according to ASTM C469 using 150 × 300mm cylindrical specimens at 28 days of age. Figure 6 presents the static modulus of elasticity results for various CNP replacement levels. With a modulus of elasticity of 31.5 GPa, the control mix matches expected values for conventional concrete of comparable strength category. This number provides a starting point for evaluation of CNP inclusion effects. Conventional concrete mixes of similar strength class, ranging from 30–33 GPa, had similar baseline values reported by (Narayanan 2021 ). With 15% CNP added, elastic modulus dropped to 27.8 GPa, or 11.7% from the control mix. Following well-established relationships between these qualities in cementitious materials, the observed decrease in elastic modulus shows strong correlation with the drop in compressive strength. This correlation matches findings by (Lustosa et al 2019), who recorded similar interactions between strength and elastic modulus in concrete containing high amount of fly ash. The decrease in elastic modulus with 15% CNP content points to changes to the material's intrinsic stress-strain behavior. (Norambuena-Contreras et al 2018 ) noted comparable effects in concrete containing metallic waste materials, assigning the variations to changes in the interfacial transition zone and matrix density. The lower elastic modulus values indicate increased deformability that may be beneficial in uses where improved strain capacity is wanted, such as in seismic-resistant constructions or components subjected to considerable thermal movements. Structural design and applications depend critically on these results. In deflection computations and serviceability limit state evaluations, especially in components where deformation control is critical, the lowered stiffness must be given thoughtful consideration. However, the slight decrease in elastic modulus at lower CNP replacement levels (10–20%) implies, that these combinations would still be useful for several structural uses and would provide environmental advantages by means of cement lowering. Sakthivel et al ( 2019 ) provide evidence for this conclusion since they show effective structural uses of concrete with same ranges of elastic modulus decrease when combining industrial byproducts. 3.6 Water Absorption Analysis Concrete's resistance to environmental exposure and durability depends critically on its water absorption properties. Figure 7 shows the absorption test findings for control and CNP-modified samples at seven and twenty-eight days, so highlighting notable changes in water absorption pattern with rising CNP content. At seven days, the control mixture showed the highest initial absorption of 31.40%, then dropped to 22.07% at 28 days. This 29.7% reduction in absorption capacity fits with normal cement hydration behavior, in which ongoing pore refinement results in decreased permeability over time. Including CNP caused a consistent decrease in 7-day absorption measurements; higher CNP content matched with lower absorption rates. This pattern implies that CNP particles may be occupying void spaces in the matrix, hence at early ages producing a denser microstructure. The disparate behavior at 28 days, when CNP-modified mixtures exhibited greater absorption relative to their 7-day levels, is a remarkable feature of the findings. With 58.7% of the 30% CNP blend showing the most noticeable increase, the degree of this rise was dependent on CNP content. The interaction between CNP particles and cement hydration products might explain this phenomenon, which could result in the development of new pore networks over time. Higher CNP content's improving absorption could suggest the formation of interfacial transition zones near CNP particles. Moreover, the gradual rise in absorption could imply possible chemical instability of CNP particles in the alkaline cement environment, which would cause pore structure changes over time. Particularly in the control combination at seven days (± 13.12%) and the 30% CNP mixture at twenty eight days (± 12.54%), the standard deviation figures reveal noteworthy variability. Variability in compaction and void content as well as heterogeneous distribution of CNP particles and particle size distribution could be responsible for this variability. The observed absorption properties have several ramifications for practical uses. The heightened long-term absorption in CNP-modified blends points to possible susceptibility to water-related degrading processes. The different absorption qualities imply that CNP-modified concrete may be better suited for particular uses where initial low absorption is critical but long-term exposure to moisture is restricted. These results stress the need for additional research on long-term absorption behavior beyond 28 days, the correlation between absorption characteristics and other durability criteria, and possible solutions for stabilizing the absorption behavior of CNP-modified mixtures. Optimization of CNP Content in Cement-Based Composites By means of thorough assessment of mechanical properties and durability characteristics, this study determined the best CNP content that joints performance criteria with environmental advantages obtained by cement reduction. At both seven and twenty-eight days of curing, the evaluation approach combined several parameters including resistance to compression, flexural resistance, resistance to tensile cracking, elastic modulus, and water absorption characteristics. The best content for cement-based composites, according to experimental data shown in Fig. 8, is a 10% CNP replacement level. The drop in mechanical properties at this replacement level stays within acceptable engineering tolerances. While the elastic modulus showed a slight reduction of 5.4%, the 28-day compressive strength had an 8.2% decrease. Although more pronounced in tensile characteristics, the 15.3% decline in flexural strength and the 12.8% reduction in splitting tensile strength are judged acceptable for a range of structural uses while providing significant cement reduction. With water absorption at 7 days (19.83%) much lower than the control mixture (31.40%), the 10% CNP combination showed improved early-age performance from a durability angle. Though absorption at 28 days (21.47%) showed a minor increase, this number was still close to the control sample (22.07%), therefore long-term durability performance was maintained. This optimization has great environmental effects because a 10% decrease in cement content results in proportional reductions in CO2 emissions linked to cement manufacture. This accomplishment fits with current building material development sustainability objectives. Higher replacement levels specifically 20% and 30% showed more notable deterioration in mechanical properties and showed worrisome trends in durability characteristics, especially in long-term water absorption behavior. These higher replacement levels, despite their greater potential for environmental effect reduction, compromised critical performance characteristics beyond acceptable limits for structural uses. The best answer seems to be a 10% replacement level since it fairly combines mechanical performance, durability needs, and environmental concerns. 4.0 CONCLUSION The practicality of using Cashew Nut Powder (CNP) as a partial substitute for cement in cement-based compounds has been thoroughly investigated in this study. Through a series of rigorously planned experiments and analyses, the study effectively focused on its fundamental goals, added significantly into the material behavior, performance limitations, and possible uses of CNP in concrete technology. CNP's physical, chemical, and mineralogical characterization showed an unusual chemical composition with high levels of tin and antimony and mostly coarse particle size distribution, few levels of reactive oxides like silicon and calcium. These results suggest CNP has little pozzolanic activity and mostly serves as a filler material rather than a geopolymeric cementitious material. With no major reactive elements present, the concrete's pozzolanic reactions help to build its strength and durability by means of which tests of mechanical properties showed clearly lower values for modulus of elasticity, Resistance to tensile cracking, flexural resistance, and resistance to compression when CNP was included. At higher replacement levels, these cuts were especially noticeable. For example, compressive strength fell by 43.8% at 10% CNP replacement; tensile and flexural strengths at 15% CNP replacement dropped by 63% and 59%, respectively. Even though strength numbers dropped with higher CNP content, statistical analysis verified that the observed trends were greatly affected by both replacement level and curing age. These results indicate that although CNP has some negative impact on mechanical performance, appropriate dosing and mix design can help to control the degree of these effects. Durability tests showed different behavior. While CNP-modified samples showed rising absorption levels with age, control specimens showed predicted declines in water absorption over time. Notably, the 30% CNP blend showed a 58.7% rise in water absorption between 7 and 28 days, which raises questions about its long-term stability especially in moisture-sensitive applications. This implies that increased CNP levels might negatively impact the concrete's resistance to water ingress and related damage mechanisms. The research found 10% as the best CNP replacement level despite the constraints. The concrete showed a tolerable drop in mechanical performance at this dosage, yet it preserved same durability qualities as the control. Particularly in light of the possible advantages of using agricultural waste and lowering cement use, this offers a practical compromise between environmental sustainability and performance. In conclusion, this study affirms that although CNP cannot completely replace cement without impacting performance, it can be used in small amounts to partly replace cement in non-structural applications. The study stresses the need of cautious material selection, dosage optimization, and performance validation when using alternative binders into cementitious systems. These results meaningfully advance the development of sustainable concrete materials and provide a basis for future inventions in green building technologies. Declarations Clinical Trial number Not applicable Ethics Approval: Not applicable Informed Consent: Not applicable Consent to Publish: Not applicable Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research did not receive any specific funding. Author Contribution WOA: Conceptualization, Methodology, Supervision,TAW: Investigation, Data curation, Formal analysis, Writing - original draftJIB: Validation, Investigation, Writing - review & editing Data Availability All data generated or analysed during this study are included in this published article References ACI 211.1-91. (2002). Standard practice for selecting proportions for concrete. American Concrete Institute. 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International Journal of Mathematical, Engineering and Management Sciences, 8 (4), 612–631. https://doi.org/10.33889/IJMEMS.2023.8.4.035 Kilani, A., Fapohunda, C., Adeleke, O., & Metiboba, C. 2022. Evaluating the effects of agricultural wastes on concrete and composite mechanical properties: A review. Research on Engineering Structures and Materials , 8(2), 189-205. https://doi.org/10.17515/resm2021.339st0912 Kumar, N., & Satyanarayana, S. 2022. Agricultural waste ash in the domain of sustainable concrete. International Journal of Health Sciences , 6(S2), 5097. https://doi.org/10.53730/ijhs.v6ns2.5097 He, J., Kawasaki, S., & Achal, V. (2020). The utilization of agricultural waste as agro-cement in concrete: A review . Sustainability, 12 (16), 6971. https://doi.org/10.3390/su12176971 Lustosa, P. R., & Magalhães, M. S. (2019). Influence of fly ash on the compressive strength and young’s modulus of concrete. Academic Journal of Civil Engineering, 37(2), 107-111. https://doi.org/10.26168/icbbm2019.15 Madurwar, M. V., Ralegaonkar, R. V., & Mandavgane, S. A. 2013. Application of agro-waste for sustainable construction materials: A review. Construction and Building Materials , 38, 872-878. https://doi.org/10.1016/j.conbuildmat.2012.09.011. Mohammed, S. A., Shakor, P., Rauniyar, A., Krishnaraj, L., & Singh, A. K. 2023. An environmental sustainability roadmap for partially substituting agricultural waste for sand in cement blocks. Frontiers in Built Environment , 9, 1214788. https://doi.org/10.3389/fbuil.2023.1214788 Muleya, F., Tembo, C., Lungu, A., & Muwila, N. (2021). Partial replacement of cement with rice husk ash in concrete production: An exploratory cost-benefit analysis for low-income communities. Engineering Management in Production and Services, 13 (1), 127–141. https://doi.org/10.2478/emj-2021-0026 Narayanan, Subramanian. (2021). Elastic modulus of concrete . Retrieved from https://www.researchgate.net/publication/352863356_Elastic_Modulus_of_Concrete N. I. M. Nadzri, J. B. Shamsul, M. N. Mazlee (2017). Development and properties of composite cement reinforced coconut fiber with the addition of fly ash. arXiv preprint arXiv:1705.00179. https://arxiv.org/abs/1705.00179 Norambuena-Contreras, J., González-Torre, I., & García-González, C. (2018). Effect of metallic waste addition on the physical and mechanical properties of cement-based mortars. Applied Sciences , 8(6), 929. https://doi.org/10.3390/app8060929 Ogundipe, A. A., Okereke, U., & Akintayo, S. 2020. Emissions and environmental implications of cement production in Nigeria. Environmental Research Letters , 15(11), 114027. https://doi.org/10.1088/1748-9326/abb9d7 Onikeku, O., Shitote, S. M., Mwero, J., & Adedeji, A. A. (2019). Evaluation of characteristics of concrete mixed with bamboo leaf ash. The Open Construction & Building Technology Journal , 13 (1), 67–80. https://doi.org/10.2174/1874836801913010067 Priyadharshini, B., Ramar, N., Eshanthini, & Kumaar, C. (2021). Experimental study on concrete with partial replacement of coarse aggregate with cashew shells. Journal of Physics: Conference Series, 1770 (1), 012036. https://doi.org/10.1088/1742-6596/1770/1/012036 Sakthivel, T., Gettu, R., & Pillai, R. G. (2019). Compressive strength and elastic modulus of concretes with fly ash and slag. Journal of The Institution of Engineers (India): Series A , 100(1), 1–12. https://doi.org/10.1007/s40030-019-00376-w Souza, A., Ferreira, H., Vilela, A., Viana, Q., Mendes, J., & Mendes, R. (2021). Study on the feasibility of using agricultural waste in the production of concrete blocks. Journal of Building Engineering, 42 , 102491. https://doi.org/10.1016/j.jobe.2021.102491 Qureshi, T. (2015). Waste metal for improving concrete performance and utilization as an alternative reinforcement bar. International Journal of Engineering Research and Applications (IJERA) , 5(2), 97–103. Retrieved from https://www.ijera.com Zhang, X., Tang, Z., Ke, G., & Li, W. (2021). Mechanical Properties and Durability of Sustainable Concrete Containing Various Industrial Solid Wastes. Transportation Research Record, 2675(12), 797-810. https://doi.org/10.1177/03611981211031236 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor assigned by journal 03 Jun, 2025 Editor invited by journal 03 Jun, 2025 Submission checks completed at journal 02 Jun, 2025 First submitted to journal 02 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6604841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466089997,"identity":"c2116543-4f7f-4ad0-aec2-2ba4d79882d2","order_by":0,"name":"Wasiu Olabamiji Ajagbe","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Wasiu","middleName":"Olabamiji","lastName":"Ajagbe","suffix":""},{"id":466089998,"identity":"fef77ce5-4fe7-4d53-b61a-1f02605006b3","order_by":1,"name":"Tirimisiy Adeniyi Wasiu","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Tirimisiy","middleName":"Adeniyi","lastName":"Wasiu","suffix":""},{"id":466090000,"identity":"2cbfbac9-d3a4-488e-b2b9-25f363eb63e3","order_by":2,"name":"John Igeimokhia Braimah","email":"data:image/png;base64,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","orcid":"","institution":"Bells University of Technology","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Igeimokhia","lastName":"Braimah","suffix":""}],"badges":[],"createdAt":"2025-05-06 15:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6604841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6604841/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84009838,"identity":"10ab8814-c059-4254-af31-3328e500688d","added_by":"auto","created_at":"2025-06-05 16:12:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":67041,"visible":true,"origin":"","legend":"\u003cp\u003eSieve Analysis Results for CN Powder\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/6d517ed4d3bd5ab2bba68f83.png"},{"id":84009839,"identity":"a8754e71-a768-4b41-9a24-36aa7a9939ae","added_by":"auto","created_at":"2025-06-05 16:12:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39630,"visible":true,"origin":"","legend":"\u003cp\u003eElemental Profile of CNP\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/6dec1bb8a73bd61cd9b74a9f.png"},{"id":84009845,"identity":"bd46de6a-2ac9-4896-9564-d89cbdb82d2b","added_by":"auto","created_at":"2025-06-05 16:12:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompressive Strength Results at Seven and Twenty-eight Days\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/4e7f73bdd0cb79d373063130.png"},{"id":84010220,"identity":"13802cb3-c424-4ece-a597-b8f1aef7fcdf","added_by":"auto","created_at":"2025-06-05 16:20:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59600,"visible":true,"origin":"","legend":"\u003cp\u003eSplitting Tensile Strength and Specimen Weight Results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/b0c5d58b4293386c20f1c318.png"},{"id":84009840,"identity":"38794ff8-89a1-4610-b4dc-aeaa0c4ae6bc","added_by":"auto","created_at":"2025-06-05 16:12:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41753,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlexural Strength Development Results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/d3284377f9b6dbe9d8c53d3f.png"},{"id":84009842,"identity":"86a92839-1fbb-4530-b61d-d54deddb7a95","added_by":"auto","created_at":"2025-06-05 16:12:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31783,"visible":true,"origin":"","legend":"\u003cp\u003eStatic Modulus of Elasticity Result\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/fcef319649f0808a1d861268.png"},{"id":84011443,"identity":"7b2c39ec-25a1-4e0f-88ef-1d1f78858b29","added_by":"auto","created_at":"2025-06-05 16:36:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":79870,"visible":true,"origin":"","legend":"\u003cp\u003eWater Absorption Test Results\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/495cc7f19257524d853d3f3e.png"},{"id":84010224,"identity":"4adb557a-dff5-4d2b-9275-2f6c01704af9","added_by":"auto","created_at":"2025-06-05 16:20:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":65944,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal optimization results\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/0378f30f2f0a8e5babf161d7.png"},{"id":84012270,"identity":"b857264d-8c13-4b2c-8394-585d9c37bf99","added_by":"auto","created_at":"2025-06-05 16:45:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1899751,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6604841/v1/71384eff-7ddb-42de-8707-171e1addabfb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssessing the Mechanical Properties of Cement-Based Composite Material with Partial Replacement of Cement by Sundried Cashew Nut Powder\u003c/p\u003e","fulltext":[{"header":"1.0 INTRODUCTION","content":"\u003cp\u003e\u0026ldquo;Over last few years, the global cement industry has grown fast, therefore aggravating greenhouse gas emissions and environmental damage. Global cement production has significantly risen over recent decades from less than 1 gigatons to nearly 2.5 gigatons contributing to approximately a quarter of total industrial CO₂ emissions. In regions like the Middle East and Africa, where cement production rose from 0.07 Gt to 0.26 Gt over a particular period, this growth is primarily driven by increased urbanization and infrastructure development in developing nations\u0026rdquo; (Chen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). With an installed production capability of fifty-eight million and nine hundred thousand metric tonnes (MMT) per year, Nigeria is the largest cement maker in the West of Africa and is projected to have a production capacity of 53 MMT by 2040 but this growth has significant effects on public health and the environment. With about 80% of national CO₂ emissions coming from fossil fuels, cement manufacture in Nigeria mostly depends on them; exposure to particulate particles (PM2.5) from cement plants has caused major health risks mostly among children under five (Etim et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) contributing to 49,100 premature death in 2017 alone.\u003c/p\u003e \u003cp\u003eWhile actions like enhancing energy efficiency and adopting alternative fuels has been proposed to reduce these effects, they are usually either inadequate or difficult to put into practice. (Benhelal et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) notes that carbon capture and storage (CCS) remain mostly at the experimental or pilot level. Thus, it's urgently required to investigate locally accessible substitutes for conventional Portland cement that are sustainable. An intriguing idea is including agricultural waste materials into concrete manufacturing. In countries like Nigeria which are rich in agriculture, such waste materials are plentiful, renewable, and inexpensive therefore they are perfect fit for sustainable construction (Madurwar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Agricultural wastes including sugarcane bagasse ash (Kilani et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), rice husk ash (Kumar and Satyanarayana, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), coconut shell ash (Kanjawane and Wagh \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), palm oil fuel ash (He et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and even coffee husks and pistachio shells (Souza et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) have been shown in earlier studies to work very well in the improvement of load bearing characteristics and performance qualities of cementitious composite and also lowering negative environmental impact.\u003c/p\u003e \u003cp\u003eAmong Nigeria's numerous agricultural resources, cashew nut production is remarkable for both its volume and economic importance. With an annual output of around 660,000 metric tonnes almost 30% of the continent's total output Nigeria is Africa's second-largest cashew nut producer (Agada and Sule, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u0026ldquo;About sixty percent of this national output comes from Kogi State alone. Generating between \u003cspan\u003e$\u003c/span\u003e253\u0026nbsp;million and \u003cspan\u003e$\u003c/span\u003e535\u0026nbsp;million yearly, the cashew sector is also economically quite important. It supports over 50,000 farmers and more than 55,000 people in processing and distribution\u0026rdquo; (Eze et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, significant garbage is produced by cashew nut processing, especially cashew seed shells and powders, which is typically thrown away or burned, causing more environmental load. With early results showing enhancements in concrete's compressive strength and durability and also a decrease in its total carbon footprint (Priyadharshini et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), recent studies suggest that this cashew nut waste can be processed into a very fine powder and utilized as a viable alternative for cement. This goes along the ideas of a very rounded economy, therefore encouraging resource efficiency and waste valorization (Ada et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these promising developments, comprehensive studies on the effects of cashew nut powder on concrete performance, especially within the Nigerian context, remain limited. Critical aspects such as optimal replacement ratios, long-term performance, and standardized durability metrics have not been adequately addressed. This paper aims to close the prevailing knowledge deficit by taking a critical look at the load bearing characteristics, performance under load and longevity of cementitious composite that incorporate partial cement substitution with sundried cashew nut powder (CNP), considering the pressing need to adopt more environmentally friendly construction practices and the economic importance of cashew cultivation in Nigeria. The study intends to define the physical, chemical, and mineralogy features of CNP, assess its effect on flexural resistance, resistance to compression, Stiffness (Elastic Modulus) and resistance to tensile cracking at seven and twenty-eight days while using CNP as a viable alternative cement substitute in varying proportions from 0\u0026ndash;30% in 10% increments, analyze durability through water absorption, chloride permeability, and sulphate resistance, and identify the optimal CNP substitution level that reaches an equilibrium state between performance and sustainability. In this way, the research aids Nigeria's broader goals for sustainable development, waste management, and climate resilience in construction by promoting the production of affordable, eco-friendly building materials.\u003c/p\u003e"},{"header":"2.0 RESEARCH METHOLOGY","content":"\u003cp\u003eThis section details the extensive research methods used to critically investigate the impact of Cashew Nut Powder (CNP) as a viable cement replacement. The research intends to evaluate the load bearing characteristics, durability traits, and workability of CNP-altered concrete, with the primary aim of refining the mix design for enhanced performance. The approach includes material characterization, mix design formulation, specimen preparation, and a variety of tests on both fresh and cured concrete.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Cement\u003c/h2\u003e \u003cp\u003eA primary binder that was used in the research was Standard Portland cement (OPC) conforming to ASTM C150 (2021) and BS EN 197-1 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To avoid moisture absorption and deterioration, the supplier at Agbowo area, Ibadan sourced the cement and stored it in air-tight containers in a dry, cold location.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Sundried Cashew Nut Powder (CNP)\u003c/h2\u003e \u003cp\u003eCashew nut shells were collected from three local cashew processing factories in Ejigbo and Ede of Osun State. This diverse sourcing aimed to ensure a representative sample of the region's cashew waste. The collected nut shells underwent a rigorous cleaning process. Initially, they were sorted by hand to remove any foreign substances, and subsequently put in clean water so as to wash it and eliminate any traces of dirt and dust. The cleaned shells were subsequently laid out on plastic sheets and dried in the sun for 28 days, with periodic turning to guarantee even drying. The moisture levels were checked each day with a portable moisture meter until they stabilized under 5%. The shells that were dried were subsequently crushed into a powder like substance by using a pestle and mortar and a milling machine. The resulting powder was sieved through a 75 \u0026micro;m (No. 200) ASTM standard sieve using a mechanical sieve shaker. The fraction passing through the sieve was collected and stored in airtight containers for use in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Fine Aggregate\u003c/h2\u003e \u003cp\u003eFine aggregate that was used was river sand in line the specification to ASTM C33/C33M (2018). The material was obtained from a neighborhood supplier located near to the University of Ibadan. The sand was dispersed on a clean concrete platform and air-dried for 48 hours to achieve a saturated surface-dry (SSD) condition. After that, potable water was used to completely clean it and get rid of organic debris, mud, and clay as well as pollutants. Using sieves ranging from 4.75 mm to 0.075 mm, the particle size distribution was calculated according to ASTM C136/C136M (2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Coarse Aggregate\u003c/h2\u003e \u003cp\u003eThe granite stone was broken down to a diameter in which the maximum was twenty millimeters. An Oyo State, Ibadan local supplier provided the granite. The stones were air-dried to a (SSD) condition then cleaned to remove pollutants and dust. Using sieves ranging from 25 mm to 4.75 mm, ASTM C136/C136M (2019) evaluated the coarse aggregate's particle size distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 Water\u003c/h2\u003e \u003cp\u003eCombining the aggregates, CNP and cement and curing the cementitious composite samples was done using potable water from the UI (University of Ibadan) water system, conforming with ASTM C1602/C1602M (2018). To make sure the standard was met, the water was tested for pH, chloride content, and total dissolved solids.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Method\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Mix Design\u003c/h2\u003e \u003cp\u003eThe cementitious composite mixes were designed in line with ACI 211.1\u0026ndash;91 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) guidelines. A control mix using 100% OPC targeted a twenty-eight-day resistance to compression strength of 40 MPa. To assess the effect of Cashew Nut Powder (CNP), the water-to-binder ratio was maintained at 0.40 across all the mixes. CNP replaced cement at 0%, 10%, 20%, and 30% by weight, with an additional 15% replacement mix included for flexural and tensile strength evaluation. Mix proportions, presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, were adjusted as needed to maintain uniform workability, verified by slump testing in accordance with ASTM C143/C143M (2020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcrete Mix Proportions (kg/m\u0026sup3;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMix ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFine Aggregate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoarse Aggregate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNP15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNP20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNP30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Mixing procedure\u003c/h2\u003e \u003cp\u003eFollowing a set mixing sequence to guarantee batch consistency, concrete was made using a 50-liter capacity lab drum mixer. Initially, the aggregates were dry-mixed for one minute. Half of the entire mixing water was added next, and blending went on for another minute. Then the cement with Cashew Nut Powder (CNP) when appropriate was added and the mixture was two minutes of stirring. The remaining water was then added to correct the workability, then two minutes of mixing continued. Two minutes' waiting for the combination started the hydrating process; a final two-minute mixing cycle was then carried out. To lessen environmental variance, all mixing was carried out at a regulated room temperature of roughly 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C. Each batch's total mixing cycle lasted around 8 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Casting and curing\u003c/h2\u003e \u003cp\u003eThe slump test was used to determine the workability of the cementitious composite mixture straight after mixing in accordance with ASTM C143/C143M (2020). The newly mixed material was cast into pre-lubricated steel or wooden molds according on the exact test requirements. To get rid of trapped air, each layer was compacted for fifteen seconds using a vibrating table running at 50 Hz. A steel trowel leveled and smoothed the specimens' top surface. Following molding, plastic sheets were used to cover the molds, which were then kept at a regulated ambient temperature of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for 24 hours. After being demolded, the specimens were put in a water-curing tank kept at 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, in line with ASTM C192/C192M (2019). To preserve consistency and cleanliness, the curing water was changed once weekly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Sample sizes and replicates\u003c/h2\u003e \u003cp\u003eIn order to ensure accurate and meaningful results, multiple specimens were prepared for each test and Cashew Nut Powder (CNP) replacement level. A total of 60 cubes were cast for the resistance to compression strength tests for 15 per replacement level (0%, 10%, 20%, 30%), with three tested at seven days and three at twenty-eight days for each level. Flexural strength was evaluated using prisms, with six beams produced three for the control and three for 15% CNP replacement. For the resistance to tensile cracking test, six cylindrical specimens were cast, divided equally between 0% and 15% CNP content. Durability assessments, including Rapid Chloride Permeability Test (RCPT) and water absorption, involved 12 specimens three per replacement level. RCPT used 100 mm \u0026times; 200 mm cylinders, while 100 mm cubes were used for modulus of elasticity and water absorption. Careful scheduling of casting and curing ensured all samples were available at the required testing ages, providing reliable data for robust statistical analysis of CNP\u0026rsquo;s impact on concrete performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Slump Test\u003c/h2\u003e \u003cp\u003eIn line with the ASTM C143/C143M-20 the slump test was carried out to measure the workability and consistency of fresh concrete immediately after mixing. Concrete was poured into a standard slump cone in three equal layers, each was then compacted with twenty-five strokes by utilizing steel rod with a diameter of 16mm. The top was leveled, and the cone was then removed vertically within 5\u0026thinsp;\u0026plusmn;\u0026thinsp;2 seconds. The slump was measured as the vertical distance between the displaced center of the concrete\u0026rsquo;s top surface and the top of the mould, recorded to the nearest 5 mm. The process was finished within two and a half minutes. Tests were performed for all mixes including control and CNP replacements at 10%, 20%, and 30% with three trials per mix. The final result was taken as the mean value. While the ratio of water-to-binder remained constant at 0.40, superplasticizer dosage was adjusted to maintain uniform workability across mixes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Compressive Strength Test\u003c/h2\u003e \u003cp\u003eCompressive strength which is also referred to as resistance to compression tests was performed in line with ASTM C39/C39M-21 using 100 mm cubic concrete specimens. After putting the specimen in water at a temperature of about 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, samples were then tested at seven and twenty-eight days so as to monitor strength development. Before testing, each specimen was surface-dried, measured and then weighed. A compressive load was applied at a controlled rate of 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 MPa/s until failure, and strength was evaluated by dividing the peak load by the cross-sectional area. Each CNP replacement level (0%, 10%, 20%, and 30%) was assessed using three specimens per age group, with average values reported.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.2.7 Flexural Strength Test\u003c/h2\u003e \u003cp\u003eThe Flexural strength which is also referred to as the modulus of rupture and flexural resistance, was measured using the three-point bending method in line with ASTM C78/C78M-21. Prismatic were cast and cured in a manner similar to compressive strength samples. After twenty-eight days, the beams were taken from the tank used for curing, dried, and measured. Each beam was positioned on support blocks with the load applied to the cast face. Loading was applied steadily until the beam failed. The peak load was noted, and flexural strength was computed by utilizing a standard equation. Three beams were tested for each mix, and the mean value was reported. Only the control mix and the mix with 15% CNP replacement were tested for flexural strength.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.2.8 Splitting Tensile Strength Test\u003c/h2\u003e \u003cp\u003eThe splitting tensile strength also referred to as the resistance to tensile cracking test followed ASTM C496/C496M-17 using a cylindrical specimen. After twenty-eight days of curing, samples were air-dried, measured, and tested. Placed between the specimen was a thin plywood strip and the machine platens so as to evenly distribute the load. A continuous load was applied until the sample reached failure along its vertical axis. Splitting tensile strength was evaluated based on the specimen dimension and maximum load. Three specimens were tested for each mix, and average values were recorded. Only the control mix (0% CNP) and the 15% CNP mix were evaluated for this test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.2.9 Durability Tests\u003c/h2\u003e \u003cp\u003eEspecially in hostile surroundings, concrete performance depends critically on durability. Two important durability metrics which are chloride ion penetration resistance and water absorption were the focus of this investigation. These tests aimed to see how adding Cashew Nut Powder (CNP) might affect the long-term performance and serviceability of concrete constructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.2.10 Water Absorption Test\u003c/h2\u003e \u003cp\u003eCubic samples of dimension (100mm) were created for this test, and the process adhered to ASTM C642-13. Concrete's porosity and permeability which are two important elements influencing its durability, were to be evaluated. The samples were dried in the oven at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C till a constant mass was achieved after twenty-eight days of curing. Then they were put in water at 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C for forty-eight hours. The specimens were surface-dried with a wet towel after immersion, and their saturated weight noted. The percentage rise in weight relative to the dry mass was used to estimate the water absorption. For every CNP replacement ratios (0%, 10%, 20%, and 30%), three samples per mix were analyzed and the average water absorption recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.2.11 Data Analysis\u003c/h2\u003e \u003cp\u003eTo guarantee the accuracy of findings, the data acquired from mechanical and durability tests were statistically examined. A minimum of three specimens were tested for each test parameter; means were computed together with their related standard deviations. To evaluate the variability of test findings, the coefficient of variation (COV) was established. Data visualization and regression analyses were done using Microsoft Excel 2019; statistical analyses were done by making use of IBM SPSS Statistics Version 26.0.\u003c/p\u003e \u003cp\u003eUsing Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the relative variations in mechanical properties were determined as percentage differences from the control mixture (0% CNP):\u003c/p\u003e \u003cp\u003eRelative Change (%) = [(Test Value - Control Value) / Control Value] \u0026times; 100 (1)\u003c/p\u003e \u003cp\u003eTo assess the temporal evolution of absorption features, the water absorption data were examined at seven and twenty-eight days for durability evaluation. Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was used to compute the absorption shift:\u003c/p\u003e \u003cp\u003eAbsorption Change (%) = [(28-day Value \u0026minus;\u0026thinsp;7-day Value) / 7-day Value] \u0026times; 100 (2)\u003c/p\u003e \u003cp\u003eTo assess the statistical significance of noticed variations among several CNP replacement levels, at a 95% confidence level (α\u0026thinsp;=\u0026thinsp;0.05), variance analysis (ANOVA) was carried out. Factors having p-values less than 0.05 were seen as statistically relevant; the relevance of each factor was evaluated depending on its F-value and p-value. An afterward Tukey's HSD test was performed to highlight notable variances among certain replacement levels.\u003c/p\u003e \u003cp\u003eRegression analysis was conducted to create mathematical models that illustrate the relationship between the process variables (CNP content, water-to-binder ratio, and curing duration) and the response variables (flexural strength, resistance to compression and splitting tensile strength, water absorption, and chloride penetration). The standard expression of the second-degree polynomial equation utilized for the modeling is shown in Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β₀ + β₁₂X₁X₂ + β₂X₂ + β₁X₁ + β₁₁X₁\u0026sup2; + β₂₂X₂\u0026sup2; (3)\u003c/p\u003e \u003cp\u003eIn this context, Y denotes the anticipated response, β₀ is the constant term, while β₁ and β₂ are the linear terms. The coefficient β₁₂ indicates interaction effects, and β₁₁ and β₂₂ represent the quadratic terms. The variables X₁ and X₂ signify the coded values for CNP content and curing duration, respectively.\u003c/p\u003e \u003cp\u003eThe effectiveness of the created models was assessed through the coefficient of determination (R\u0026sup2;), adjusted R\u0026sup2;, and predictive R\u0026sup2;. A lack of fit test was conducted to evaluate the appropriateness of the models, with a non-significant lack of fit (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) suggesting a strong alignment between the model and the data. The optimization of CNP content was determined through a comprehensive evaluation of mechanical and durability parameters, considering the acceptable threshold values for structural applications. Maximum allowable strength reduction limits, durability performance indicators, and environmental advantages attained by cement reduction were all included in the assessment criteria.\u003c/p\u003e \u003cp\u003eUsing Origin Pro 2023b, graphical representations helped to accomplish results visualization. Mean values and standard deviations at various CNP replacement levels were shown using bar graphs with error bars to display the mechanical qualities. The relationship between several mechanical features and their relative variations with rising CNP content was depicted using combined bar-line graphs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.0 RESULTS AND DISCUSSION","content":"\u003cp\u003e \u003cb\u003eThe results of the work carried out in this investigation are presented in this chapter together with a thorough discussion of them. To enable easy interpretation, the results of several tests and evaluations are shown in tables and graphs. The discussion section closely analyzes these findings, contrasts them with earlier research, and investigates their consequences for the utilization of (CNP) as a extra cementitious ingredient in concrete manufacture.\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sieve Analysis\u003c/h2\u003e \u003cp\u003eThe Sieve analysis test which was done in a way that aligns with ASTM C136/C136M-19 revealed the particle size distribution of the Cashew Nut powder (CNP). For a 200g sample of CNP powder the data for the sieve analysis are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSieve Analysis Results for CN Powder\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSieve Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight Retained (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Retained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative % Retained\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Passing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.75 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.36 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.18 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e600 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e425 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e212 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe sieve analysis results indicated that most of the CNS powder particle remained between the 600 \u0026micro;m and 2.36 mm sieves. On the 600-um sieve, 33.95% was kept; on the 1.18 mm sieve, 43.00% was retained. This suggests that the CNS powder consists mostly of medium to coarse particle sizes. Having a cumulative percentage passing through the 4.75 mm sieve of 97.05%, the material may be classified under ASTM C33 requirements as a fine aggregate. Notably, only 1.15% of the particles passed the 600 \u0026micro;m sieve, suggesting that the CNS powder contains few extremely fine particles. Since a greater proportion (64.90%) of the material is coarser than 1.18 mm, the particle size distribution may not match the conventional gradation requirements for concrete's fine aggregates. This property might influence how the powder will behaves in concrete mixes, therefore impacting water needs and application simplicity. By summing the cumulative percentage retained on standard sieves and dividing by 100, a fineness modulus of 3.88 was achieved. This figure, exceeding the typical range of 2.3 to 3.1 for aggregates(fine) used in concrete, highlights the coarser nature of the CNS powder.\u003c/p\u003e \u003cp\u003e \u003cb\u003eX-Ray Fluorescence (XRF) Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003e(XRF) analysis which is a non-destructive analysis. Quantitative assessment of the elemental makeup of the Cashew Nut Powder (CNP) was obtained using XRF analysis. This study provides important information on the possible behavior of CNP as a supplementary cementitious component. Using an XRF spectrometer run at 40.0 KV and 350 \u0026micro;A, with a 100-second test duration, the examination was carried out. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the XRF findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen compared to conventional supplementary cementitious materials (SCMs) like fly ash, ground granulated blast furnace slag (GGBS), or silica fume, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates a distinct elemental profile for CNP.The spectrum shows a broad range of components; their relative abundances in the sample correspond to different peak intensities. The great tin (Sn) concentration of 4.6144% and antimony (Sb) 3.9040% that stand out most in the composition are Though these components are seldom found in such concentrations in concrete-compatible materials, they appear as separate peaks toward the right end of the spectrum. Their significant presence calls for close attention since it overwhelms the basic CNP profile.\u003c/p\u003e \u003cp\u003eSuch high levels of tin might have a marked influence on the qualities of concrete blends containing CNP. Known to disrupt cement hydration processes, tin compounds could slow the curing time of cement pastes and thereby influence the early strength development of concrete. CNP's effect on setting times and strength development patterns in concrete mixtures must be carefully studied because this potential for changing hydration kinetics calls for it. Likewise, the great antimony content causes worries about its effects on concrete characteristics and environmental safety. The clear peak for antimony in the spectrum highlights its remarkable abundance, which is unusual in Standard SCMs. This underscores the importance of thorough safety evaluations, especially about possible leaching behavior of antimony in concrete matrices.\u003c/p\u003e \u003cp\u003eAmong the little quantities found, potassium (K) is especially interesting at 0.6984% since a clear peak can be seen in the left part of the spectrum. Although not very high, this amount of potassium helps to determine the alkali content of the substance, which is essential in the chemistry of concrete. When estimating the overall alkali content of concrete blends to reduce possible alkali-silica reaction (ASR) hazards, the potassium concentration in CNP should be closely taken into account.\u003c/p\u003e \u003cp\u003eThe spectrum also reveals peaks for iron (Fe), copper (Cu), and zinc (Zn), which match the observed levels of 0.3140%, 0.2249%, and 0.2641% respectively. The iron content might help to determine the color of the finished concrete and would have minimal influence on cement hydration, therefore affecting early strength development via its involvement in ettringite formation.\u003c/p\u003e \u003cp\u003ePresent at 0.2463%, sulfur (S) is another element of interest in cementitious systems even if its peak is less pronounced in the spectrum. Sulfur in CNP might change the sulfate balance in concrete mixes, therefore influencing early strength development and setting times.\u003c/p\u003e \u003cp\u003eEspecially low compared to conventional pozzolanic sources are silicon (Si) levels of 0.0545% and calcium (Ca) levels of 0.0417%. Their rather modest peaks on the left side of the spectrum reflect this. These elements are essential for producing calcium silicate hydrate (C-S-H), the primary compound responsible for strength in hydrated cement paste. Their low concentrations imply that CNP could not contribute much to pozzolanic reactions, hence suggesting that its role in concrete could be more as a filler than an active pozzolanic material.\u003c/p\u003e \u003cp\u003eMo shows a particularly strong peak among other elements including rubidium (Rb), zirconium (Zr), niobium (Nb), and molybdenum (Mo). Although not often important in cementitious materials, these components help to define CNP uniquely and may affect how it behaves in concrete in ways needing more study.\u003c/p\u003e \u003cp\u003eIt's also noteworthy that some elements particularly magnesium (Mg) and titanium (Ti) are absent; the absence of unique peaks for these elements in the spectrum points to this. This more distinguishes CNP from other SCMs and could affect the kinds of hydration products generated in concrete mixes with CNP.\u003c/p\u003e \u003cp\u003eCNP has several uses as a extra cementitious material given its unusual elemental makeup as clearly shown in the XRF spectrum. Limited pozzolanic action is indicated by the low amounts of silicon, aluminum, and calcium, therefore possibly implying little contribution to strength growth through pozzolanic reactions. Rather, CNP could mostly serve as a concrete filler, perhaps enhancing particle packing but not much aiding in the chemical interactions of cement hydration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Compressive Strength Development\u003c/h2\u003e \u003cp\u003eBy the twenty eight and seven days of curing, the resistance to compression evolution of CNP-modified cement-based composites was analysed. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, CNP content had a clear impact on strength growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe experimental data depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed a remarkable drop in compressive strength in the cementitious composites as CNP level rises. Specimens with CNP showed significant strength losses, whereas the control mixture reached compressive strengths of 13.57 MPa and 20.53 MPa at seven and twenty-eight days correspondingly. At 10% replacement, the compressive strength went down to 2.79 MPa at seven days and 11.53 MPa at 28 days, or 79.5% and 43.8% respectively. With 28-day strengths dropped to 5.18 MPa and 2.37 MPa, respectively, further increases in CNP content to 20% and 30% caused much more pronounced strength loss.\u003c/p\u003e \u003cp\u003eThis marked drop in strength profile differs greatly from the performance generally seen with traditional supplementary cementitious materials (SCMs). Unlike results published in the current literature for agricultural waste-based SCMs, the experimental data shows a more pronounced effect on mechanical characteristics. For instance, (Muleya et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) study on rice husk ash inclusion at comparable replacement ratios (10\u0026ndash;30%) recorded the resistance of compression of the cementitious composite decreased with increasing rice husk ash content. Specifically, the 20% replacement mix produced an optimum compressive strength of 18 MPa at a 0.5 water/binder ratio, while higher replacement levels resulted in further strength reductions. Similarly, studies by Onikeku et al (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) looked into the effects of incorporating bamboo leaf ash (BLA) into concrete mixtures at replacement levels of 0\u0026ndash;20% with a 5% increment. The findings indicated that the resistance to compression of the concrete decreased with increasing BLA content. Specifically, at twenty-eight days, the resistance to compression of the concrete with 10% BLA replacement was approximately 95% of the control mix, while the strengths for 15% and 20% BLA replacements were approximately 80% and 70% of the control mix, respectively.\u003c/p\u003e \u003cp\u003eParticularly at early ages, the remarkable strength loss matches more closely results of research on materials with great metallic content. Research by Qureshi, T. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) observed severe strength impediment in cement composites containing industrial wastes with elevated tin concentrations, reporting strength reductions of 40\u0026ndash;60% at 10% replacement levels. This correlation supports the hypothesis that the high tin and antimony content in CNP, as identified in the XRF analysis, may be significantly interfering with the cement hydration mechanisms.\u003c/p\u003e \u003cp\u003eThe strength development pattern observed in this study, characterized by particularly poor early-age strength followed by relatively higher strength gotten between seven and twenty eight days (especially in the 10% CNP mixture). This pattern suggests a potential retarding influence on cement hydration, possibly caused by the formation of metallic hydroxides that interfere with normal C3S and C2S hydration processes.\u003c/p\u003e \u003cp\u003eThe ANOVA results is depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of ANOVA Results for Compressive Strength (Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e724.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1-CNP Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e598.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2-Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1X2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the ANOVA table (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the model is highly important, with an F-value of 98.63 and a p-value of less than 0.0001. This indicates that the model provides an excellent fit for the data and that the independent variables (CNP content and age) collectively have a very serious influence on the resistance to compression of the concrete specimens. Statistical guidelines say that a high F-value in the ANOVA table means that at least one of the regression coefficients is non-zero, therefore showing a relationship that is very strong between the predictor and response variables.\u003c/p\u003e \u003cp\u003eWith a p-value under 0.0001 and an F-value of 135.69, the CNP content (X₁) has the most significant impact. This very important finding suggests that the inclusion of CNP greatly affects the concrete's compressive strength. The high F-value implies that the CNP content mostly governs the variation in compressive strength. The age element (X₂) is also extremely important; with an F-value of 69.75 and a p-value under 0.0001, indicates the curing age has a major influence on the development of resistance to compression of concrete. The high F-value suggests that age has a critical role in strength development, which is in accordance with the basic ideas of concrete curing and strength gain over time.\u003c/p\u003e \u003cp\u003eWith an F-value of 16.27 and a p-value of 0.0008, the relationship between CNP content and age (X₁X₂) is important. This implies that the CNP level that has an impact on compressive strength differs with age, and vice versa, hence indicating a synergistic connection between these elements. This interaction effect suggests that the effect of CNP content on strength growth varies depending on the curing age. The model sufficiently reflects the relationship between the variables, as shown by the low fit (p\u0026thinsp;=\u0026thinsp;0.0557). This confirms the model's accuracy in forecasting compressive strength depending on CNP content and age, therefore implying that the preferred model structure fairly depicts the underlying data relationships.\u003c/p\u003e \u003cp\u003eThe regression equation using Y\u003csub\u003e1\u003c/sub\u003e for compressive strength is given in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Y₁\\:=\\:14.26\\:-\\:0.498X₁\\:+\\:0.396X₂\\:-\\:0.009X₁X₂$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAs seen in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the regression model for compressive strength response is a linear model that connects the compressive strength (Y₁) to two input variables: age (X₂) and CNP content (X₁). The model includes both main effects and interaction terms to capture the connection between these variables and the compressive strength of concrete specimens. The CNP content (X₁) has a significant negative coefficient (-0.498), indicating that increasing CNP content results to a substantial drop in compressive strength. This strong negative relationship suggests that higher percentages of CNP in the concrete mixture results strength values that have reduced, which is clearly evidenced in the experimental data where strength decreased from 20.53 MPa in the control mixture to 2.37 MPa at 30% CNP content at 28 days.\u003c/p\u003e \u003cp\u003eThe age factor (X₂) has a positive coefficient (0.396), demonstrating that increasing curing age contributes to higher compressive strength. This positive relationship aligns with fundamental concrete behavior, where strength typically increases with age due to continued hydration processes and cement matrix development. The size of this coefficient suggests that age considerably enhances strength development; however, its influence differs with CNP content because of the notable interaction term.\u003c/p\u003e \u003cp\u003eThe interaction term (X₁X₂) has a negative coefficient (-0.009), indicating an antagonistic effect between CNP content and age. This significant interaction (as confirmed by the ANOVA p-value of 0.0008) suggests that the positive impact of age on the development of strength becomes less pronounced at higher CNP contents. This interaction helps explain why specimens with higher CNP content show different strength development patterns compared to the control mixture. The constant term (14.26) represents the baseline compressive strength when both variables are at their reference levels. This value approximates the expected strength for the control mixture at early age, providing a reasonable baseline for comparing the effects of CNP addition and age variation.\u003c/p\u003e \u003cp\u003eThe model accurately reflects the separate and joint influences of CNP content and age on compressive strength, where the important negative effect of CNP content is the primary cause of strength decline, influenced by age and the interplay between age and CNP content.\u003c/p\u003e \u003cp\u003eThe statistical data for the Compressive Strength fit is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit Statistics for Compressive Strength (Y\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient of variance (CV%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient of determination (R\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Fit Statistics table (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provides insight into the model's performance. A standard deviation (SD) of 1.21 MPa reflects how much the compressive strength values deviate from the mean, indicating good precision given the broad strength range (1.60\u0026ndash;20.53 MPa). The coefficient of variation (CV), calculated as the SD divided by the mean, is 16.29%, showing moderate variability reasonable considering the wide CNP content range (0\u0026ndash;30%) and differences between early and later curing ages.\u003c/p\u003e \u003cp\u003eThe coefficient of determination (R\u0026sup2;) of 0.969 shows that 96.9% of the variation in resistance to compression is explained by age and CNP content. This high value confirms a strong model fit. The adjusted R\u0026sup2;, which accounts for the number of predictors, is 0.958, indicating that overfitting is minimal and all predictors are relevant. A predicted R\u0026sup2; of 0.942 further confirms the model\u0026rsquo;s ability to generalize to new data. The small gap between R\u0026sup2;, adjusted R\u0026sup2;, and predicted R\u0026sup2; (all within 0.027) suggests the model is both stable and accurate.\u003c/p\u003e \u003cp\u003eLastly, an adequate precision value of 28.764, well above the acceptable threshold of 4, indicates a high signal-to-noise ratio. This shows that the model can effectively distinguish between different levels of compressive strength, making it reliable for prediction across the tested design space.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Splitting Tensile Strength Analysis\u003c/h2\u003e \u003cp\u003eThe analysis of splitting tensile strength for CNP-modified cement-based composites was assessed at both seven and twenty-eight days of curing. The results revealed a significant influence of CNP content on strength development, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe resistance to tensile cracking tests revealed significant variations between the control mixture and CNP-modified specimens across both testing ages. The control specimens (0% CNP) demonstrated strength development from 7 to 28 days, with values increasing from 2.50 MPa to 2.87 MPa. The 7-day results showed individual values ranging from 2.12 MPa to 2.88 MPa, while 28-day values ranged from 2.4 MPa to 3.3 MPa, demonstrating reasonable consistency. In contrast, the 15% CNP replacement specimens showed markedly reduced strength values at both ages, with 7-day strength averaging 0.92 MPa and increasing marginally to 1.05 MPa at twenty-eight days.\u003c/p\u003e \u003cp\u003eThe reduction in resistance to tensile cracking with 15% CNP replacement shows about a 63.2% reduction at 7 days and a 63.4% reduction at 28 days in contrast to the control mixture. This significant reduction in tensile capacity is consistent with the compressive strength trends noted earlier, although the extent of reduction seems to be very pronounced in the resistance to tensile cracking findings. The comparable reduction rates at both ages indicate that the adverse effect of CNP on tensile strength stays fairly consistent throughout the curing duration.\u003c/p\u003e \u003cp\u003eThe specimen weights provide additional insight into the material characteristics. Control specimens maintained consistent weights, showing a minimal increase from 7 to 28 days (508.25 g to 509.48 g), indicating good uniformity in the mixture proportioning and specimen preparation. The 15% CNP specimens showed significantly lower weights at both ages, averaging 299.87 g at 7 days and slightly increasing to 300.48 g at 28 days, representing approximately a 41% reduction in mass compared to the control specimens. The minimal weight changes between 7 and 28 days for both mixtures suggest stability in the hardened state properties.\u003c/p\u003e \u003cp\u003eThese findings align with prior observations (Zhang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that incorporating certain waste materials into cementitious composites often results in more significant reductions in tensile strength than in compressive strength. This effect is especially notable when such materials interfere with proper cement hydration or alter the mix composition in a way that compromises matrix integrity. The great drop in specimen weight with CNP inclusion points to notable microstructure and density variations in the material. This findings are in line with those of Nadzri et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who observed that, because agricultural waste-based additives have lower specific density than cement, they frequently result in reduced unit weight. The degree of weight loss seen in the current study (41%) is especially large compared to usual data published in literature (usually 10\u0026ndash;20% for comparable replacement levels), therefore CNP inclusion may be influencing the consolidation qualities of the matrix or producing more void space.\u003c/p\u003e \u003cp\u003eThe lack of results for the third sample in the 15% CNP mixture points to possible problems with specimen integrity or testing protocols, which could be related to the considerably lower strength and maybe handling sensitivity of the changed mixture. This finding shows issues about the material's readiness and strength.\u003c/p\u003e \u003cp\u003eThe ratio of resistance to tensile cracking to resistance to compression in normal concrete typically ranges from 8\u0026ndash;14%. In this study, the control mixture showed a ratio within this expected range. However, the CNP-modified specimens showed an altered relationship between tensile and compressive strengths, suggesting fundamental changes in the material's mechanical behavior and load-carrying mechanisms.\u003c/p\u003e \u003cp\u003eThe dramatic reduction in both strength and weight indicates that the incorporation of CNP fundamentally alters the material's physical and load bearing properties. These changes could be related to several factors: the interference of metallic components with cement hydration processes, increased porosity in the matrix, reduced bond strength between paste and aggregates, and possible changes in the pore structure and distribution. These findings suggest that applications of CNP-modified concrete would be severely limited in a situation where tensile strength is a important design consideration, such as in flexural members or elements subject to splitting forces. The ANOVA results is illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of ANOVA Results for Splitting Tensile Strength (Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-CNP Content\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e-Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLack of Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the ANOVA results confirm the model's strong significance, with a F-value of 42.13 and a p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. This suggests the model aligns effectively the data well and that the independent variables CNP content and curing age significantly influence the resistance to tensile cracking. A high F-value typically indicates that at least one predictor is meaningfully related to the response.\u003c/p\u003e \u003cp\u003eAmong the variables, CNP content (X₁) has the strongest impact, demonstrated by an F-value of 116.73 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. This highlights the major role of CNP in altering tensile strength. The age factor (X₂), with an F-value of 8.07 and p\u0026thinsp;=\u0026thinsp;0.0149, also has a statistically significant effect, though less pronounced, aligning with the expected strength gain over time.\u003c/p\u003e \u003cp\u003eThe interaction between CNP content and age (X₁X₂) is not significant (p\u0026thinsp;=\u0026thinsp;0.2311), suggesting that their effects are independent rather than combined. The lack of fit has a p-value of 0.1027, indicating it is not statistically significant and that the model adequately represents the relationship between the inputs and tensile strength.\u003c/p\u003e \u003cp\u003eThe regression equation for resistance to tensile cracking(Y₂) is presented in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Y2\\:=\\:2.83\\:-\\:0.105X₁\\:+\\:0.018X₂\\:-\\:0.001X₁X₂$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eY\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;Splitting Tensile Strength/resistance to tensile cracking (MPa)\u003c/p\u003e \u003cp\u003eX₁ = CNP Content (%)\u003c/p\u003e \u003cp\u003eX₂ = Age (Days)\u003c/p\u003e \u003cp\u003eThe regression equation for splitting tensile strength is a linear model that incorporates both main effects CNP content (X₁) and curing age (X₂) as well as their interaction (X₁X₂). A negative coefficient for CNP content (-0.105) indicates that higher CNP levels reduce splitting tensile strength, consistent with experimental data indicating a decline in strength at 15% replacement compared to the control.\u003c/p\u003e \u003cp\u003eIn contrast, the positive coefficient for age (0.018) reflects the typical strength gain of concrete over time due to ongoing hydration. The interaction term has a small negative coefficient (-0.001), implying a slight weakening of age-related strength development at higher CNP levels. However, as noted earlier, this interaction is statistically insignificant (p\u0026thinsp;=\u0026thinsp;0.2311), indicating that age and CNP content affect tensile strength largely independently.\u003c/p\u003e \u003cp\u003eThe intercept (2.83) represents the estimated baseline tensile strength at reference levels for both variables, closely matching early-age strength of the control mix. Fit statistics for this model are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit Statistics for Splitting Tensile Strength (Y\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient of variance (CV%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient of determination (R\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Fit Statistics table (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) offers insight into the model's precision and reliability. A standard deviation of 0.216 MPa reflects low dispersion in splitting tensile strength data, indicating consistent experimental results. The coefficient of variation (CV) is 11.77%, representing moderate variability, which remains acceptable considering the influence of CNP on mix consistency.\u003c/p\u003e \u003cp\u003eThe model shows a strong fit with an R\u0026sup2; of 0.927, meaning it accounts for 92.7% of the variability in splitting tensile strength. The adjusted R\u0026sup2; is 0.905, suggesting minimal risk of overfitting, while the predicted R\u0026sup2; of 0.876 confirms good model performance on unseen data. The close range among these values (within 0.051) supports both accuracy and stability.\u003c/p\u003e \u003cp\u003eAn adequate precision value of 19.842 greatly surpasses the acceptable limit of 4, indicating a robust signal-to-noise ratio and the model\u0026rsquo;s capability to distinguish between response levels effectively. Overall, these metrics validate the model\u0026rsquo;s reliability in predicting tensile strength based on CNP content and curing age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Flexural Strength Analysis\u003c/h2\u003e \u003cp\u003eThe flexural strength/ Flexural resistance assessment compared the control mixture (0% CNP) with specimens containing 15% CNP replacement. The test results revealed significant constrast in both early-age and ultimate strength development between the control and CNP-modified specimens as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn comparison to the control, the 15% CNP replacement mix had much lower strengths 1.12 MPa at seven days and 1.67 MPa at twenty-eight days resulting in a somewhat lower ratio of 0.67; early and late strengths were reduced 60.7% and 59%, respectively. The control mix had a strength development ratio of 0.70 and 2.85 MPa at 7-day flexural strength, rising to 4.07 MPa at 28 days.\u003c/p\u003e \u003cp\u003eThough flexural behavior exhibited its own unique sensitivities, CNP addition significantly influenced flexural strength, a trend also seen in tensile and compressive strength measurements. This fits with known facts since flexural strength depends mostly on tensile characteristics and the quality of the (ITZ) interfacial transition zone, which makes it more susceptible to variations in the concrete matrix.\u003c/p\u003e \u003cp\u003eSeveral factors might underlie the reduced strength and load-deflection performance observed in CNP-modified samples. The lower unit weight suggests increased porosity, which typically compromises flexural performance. Similar outcomes have been reported in studies (Norambuena-Contreras et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) involving waste-based cementitious materials, where certain component such as metallic elements might disrupt with the bond between the cement paste and aggregate, thereby weakening the overall structural integrity.\u003c/p\u003e \u003cp\u003eThis drop in flexural capacity poses questions for uses exposed to bending structurally. Deviations in the CNP mixes show basic changes in load distribution and failure behavior even if flexural strength in normal concrete is normally 10\u0026ndash;15% of compressive strength. For components such beams or pavements, this could call for design changes such as greater reinforcement or section sizes, therefore offsetting the environmental or financial advantages of CNP usage. Furthermore, changed microstructural characteristics could compromise the crack resistance of the material, which is important in flexural performance. The ANOVA results is illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of ANOVA Results for Flexural Strength (Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX₁-CNP Content\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX₂-Age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003esignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX₁X₂\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLack of Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003enot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith an F-value of 132.41 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, the ANOVA findings (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) show that the model is very significant, therefore implying that curing age and CNP content together have a major influence on concrete's flexural strength. Since a high F-value implies that at least one regression coefficient is non-zero, it supports the existence of a strong bond between the predictor variables and the response.\u003c/p\u003e \u003cp\u003eWith an F-value of 324.28 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, CNP content (X₁) had the most impact among the predictors, hence variations in CNP dose clearly affect flexural strength. Though the impact of CNP content is more pronounced, the age factor (X₂) also exhibited a strong effect with an F-value of 68.07 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, therefore underlining the significance of curing time in strength development.\u003c/p\u003e \u003cp\u003eWith a p-value of 0.0549, the interaction term (X₁X₂) had an F-value of 4.69, suggesting it not formally significant. This suggests that CNP age and content have mostly independent influences on flexural strength. The low lack of fit p-value (0.1577) further strengthens the model's reliability in projecting flexural strength depending on the two variables by confirms its adequate representation of the observed data.\u003c/p\u003e \u003cp\u003eEquation (\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) gives the regression model for flexural strength (Y₃).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{3}\\:=\\:3.76\\:-\\:0.173X₁\\:+\\:0.047X₂\\:-\\:0.002X₁X₂$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAs shown by the equation, the regression model for flexural strength (Y₃) is a linear model including both main effects and an interaction term to describe the relationship between flexural resistance, CNP content (X₁), and curing age (X₂). The negative coefficient for CNP content (-0.173) indicates that more flexural strength is lost the more CNP is added to the concrete mix. Experimental data clearly support this trend; for a 15% CNP substitution, they show a drop from 4.07 MPa in the control combination to 1.67 MPa at twenty-eight days indicating a significant decrease in structural performance.\u003c/p\u003e \u003cp\u003eThe usual response of concrete where strength rises with time because of continuous hydration and the gradual formation of the cementitious matrix is shown by the positive coefficient for age (0.047). Though this contribution is small relative to that of CNP material, it is still very important for strength growth. With a small negative coefficient (-0.002), the interaction term (X₁X₂) suggests CNP content and age have a slight oppositional connection. Its p-value (0.0549) indicates, though, that this interaction is not statistically significant, therefore strengthening the view that the two factors have mostly independent influences on flexural strength.\u003c/p\u003e \u003cp\u003eWhen both input variables are at their reference values, the constant term (3.76) denotes the baseline flexural strength. This provides a good reference point for evaluating how different CNP amount and age affect things.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e offers the flexural strength model's fit data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFit Statistics for Flexural Strength (Y\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard Deviation (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficient of determination (R\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredicted R\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCoefficient of variance (CV%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdjusted R\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdequate Precision\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eImportant information about the regression model's reliability and precision for flexural strength is found in the Fit Statistics table (Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). A (SD) of 0.173 MPa indicates how much the flexural strength values dispersed about the mean are varied. Considering the observed range of strength values (1.12 MPa to 4.07 MPa), this somewhat low value implies a high degree of accuracy in model forecasts and experimental calculations.\u003c/p\u003e \u003cp\u003e7.12% is the (CV), which is the ratio of the (SD) to the mean presented as a percentage. This low CV value suggests great consistency across the test conditions and indicates little relative variability in the flexural strength data. In concrete study, CV values under 10% usually indicate great quality, therefore confirming the dependability of the experimental methods and results.\u003c/p\u003e \u003cp\u003eWith a value (R\u0026sup2;) of 0.975, the model explains 97.5% of the variation in flexural strength. With an extraordinarily high R\u0026sup2; number, this confirms that the regression model fits well and clearly reflect the relationships between age, CNP content, and flexural strength. In materials research, a good model is usually indicated by an R\u0026sup2; value higher than 0.9.\u003c/p\u003e \u003cp\u003eWith an 0.968 adjusted R\u0026sup2; which corrects for the number of predictors and guards against overfitting the minimal difference of 0.007 between R\u0026sup2; and adjusted R\u0026sup2; suggests that the model\u0026rsquo;s predictors are all meaningful and that overfitting is not a concern.\u003c/p\u003e \u003cp\u003eA projected R\u0026sup2; value of 0.952 reinforces the capacity of the model to generalize and predict fresh observations. Strong model stability and predictive performance are seen in the tight agreement among R\u0026sup2;, adjusted R\u0026sup2;, and predicted R\u0026sup2; (all within 0.023 of one other).\u003c/p\u003e \u003cp\u003eWell above the threshold of 4 usually regarded as acceptable, adequate precision measuring the signal-to-noise ratio stands at 35.842. This very high value suggests that the model is quite reliable for design optimization and predictive uses depending on variations in CNP content and curing age as well as can successfully discriminate between several degrees of response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Modulus of Elasticity Analysis\u003c/h2\u003e \u003cp\u003eThe elastic modulus of concrete, a fundamental property governing structural deformation behavior, was evaluated according to ASTM C469 using 150 \u0026times; 300mm cylindrical specimens at 28 days of age. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the static modulus of elasticity results for various CNP replacement levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith a modulus of elasticity of 31.5 GPa, the control mix matches expected values for conventional concrete of comparable strength category. This number provides a starting point for evaluation of CNP inclusion effects. Conventional concrete mixes of similar strength class, ranging from 30\u0026ndash;33 GPa, had similar baseline values reported by (Narayanan \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With 15% CNP added, elastic modulus dropped to 27.8 GPa, or 11.7% from the control mix.\u003c/p\u003e \u003cp\u003eFollowing well-established relationships between these qualities in cementitious materials, the observed decrease in elastic modulus shows strong correlation with the drop in compressive strength. This correlation matches findings by (Lustosa et al 2019), who recorded similar interactions between strength and elastic modulus in concrete containing high amount of fly ash. The decrease in elastic modulus with 15% CNP content points to changes to the material's intrinsic stress-strain behavior. (Norambuena-Contreras et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) noted comparable effects in concrete containing metallic waste materials, assigning the variations to changes in the interfacial transition zone and matrix density. The lower elastic modulus values indicate increased deformability that may be beneficial in uses where improved strain capacity is wanted, such as in seismic-resistant constructions or components subjected to considerable thermal movements.\u003c/p\u003e \u003cp\u003eStructural design and applications depend critically on these results. In deflection computations and serviceability limit state evaluations, especially in components where deformation control is critical, the lowered stiffness must be given thoughtful consideration. However, the slight decrease in elastic modulus at lower CNP replacement levels (10\u0026ndash;20%) implies, that these combinations would still be useful for several structural uses and would provide environmental advantages by means of cement lowering. Sakthivel et al (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provide evidence for this conclusion since they show effective structural uses of concrete with same ranges of elastic modulus decrease when combining industrial byproducts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Water Absorption Analysis\u003c/h2\u003e \u003cp\u003eConcrete's resistance to environmental exposure and durability depends critically on its water absorption properties. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the absorption test findings for control and CNP-modified samples at seven and twenty-eight days, so highlighting notable changes in water absorption pattern with rising CNP content.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt seven days, the control mixture showed the highest initial absorption of 31.40%, then dropped to 22.07% at 28 days. This 29.7% reduction in absorption capacity fits with normal cement hydration behavior, in which ongoing pore refinement results in decreased permeability over time.\u003c/p\u003e \u003cp\u003eIncluding CNP caused a consistent decrease in 7-day absorption measurements; higher CNP content matched with lower absorption rates. This pattern implies that CNP particles may be occupying void spaces in the matrix, hence at early ages producing a denser microstructure.\u003c/p\u003e \u003cp\u003eThe disparate behavior at 28 days, when CNP-modified mixtures exhibited greater absorption relative to their 7-day levels, is a remarkable feature of the findings. With 58.7% of the 30% CNP blend showing the most noticeable increase, the degree of this rise was dependent on CNP content. The interaction between CNP particles and cement hydration products might explain this phenomenon, which could result in the development of new pore networks over time.\u003c/p\u003e \u003cp\u003eHigher CNP content's improving absorption could suggest the formation of interfacial transition zones near CNP particles. Moreover, the gradual rise in absorption could imply possible chemical instability of CNP particles in the alkaline cement environment, which would cause pore structure changes over time.\u003c/p\u003e \u003cp\u003eParticularly in the control combination at seven days (\u0026plusmn;\u0026thinsp;13.12%) and the 30% CNP mixture at twenty eight days (\u0026plusmn;\u0026thinsp;12.54%), the standard deviation figures reveal noteworthy variability. Variability in compaction and void content as well as heterogeneous distribution of CNP particles and particle size distribution could be responsible for this variability.\u003c/p\u003e \u003cp\u003eThe observed absorption properties have several ramifications for practical uses. The heightened long-term absorption in CNP-modified blends points to possible susceptibility to water-related degrading processes.\u003c/p\u003e \u003cp\u003eThe different absorption qualities imply that CNP-modified concrete may be better suited for particular uses where initial low absorption is critical but long-term exposure to moisture is restricted. These results stress the need for additional research on long-term absorption behavior beyond 28 days, the correlation between absorption characteristics and other durability criteria, and possible solutions for stabilizing the absorption behavior of CNP-modified mixtures.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cb\u003eOptimization of CNP Content in Cement-Based Composites\u003c/b\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e By means of thorough assessment of mechanical properties and durability characteristics, this study determined the best CNP content that joints performance criteria with environmental advantages obtained by cement reduction. At both seven and twenty-eight days of curing, the evaluation approach combined several parameters including resistance to compression, flexural resistance, resistance to tensile cracking, elastic modulus, and water absorption characteristics.\u003c/p\u003e \u003cp\u003eThe best content for cement-based composites, according to experimental data shown in Fig.\u0026nbsp;8, is a 10% CNP replacement level. The drop in mechanical properties at this replacement level stays within acceptable engineering tolerances. While the elastic modulus showed a slight reduction of 5.4%, the 28-day compressive strength had an 8.2% decrease. Although more pronounced in tensile characteristics, the 15.3% decline in flexural strength and the 12.8% reduction in splitting tensile strength are judged acceptable for a range of structural uses while providing significant cement reduction.\u003c/p\u003e \u003cp\u003eWith water absorption at 7 days (19.83%) much lower than the control mixture (31.40%), the 10% CNP combination showed improved early-age performance from a durability angle. Though absorption at 28 days (21.47%) showed a minor increase, this number was still close to the control sample (22.07%), therefore long-term durability performance was maintained. This optimization has great environmental effects because a 10% decrease in cement content results in proportional reductions in CO2 emissions linked to cement manufacture. This accomplishment fits with current building material development sustainability objectives.\u003c/p\u003e \u003cp\u003eHigher replacement levels specifically 20% and 30% showed more notable deterioration in mechanical properties and showed worrisome trends in durability characteristics, especially in long-term water absorption behavior. These higher replacement levels, despite their greater potential for environmental effect reduction, compromised critical performance characteristics beyond acceptable limits for structural uses. The best answer seems to be a 10% replacement level since it fairly combines mechanical performance, durability needs, and environmental concerns.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 CONCLUSION","content":"\u003cp\u003eThe practicality of using Cashew Nut Powder (CNP) as a partial substitute for cement in cement-based compounds has been thoroughly investigated in this study. Through a series of rigorously planned experiments and analyses, the study effectively focused on its fundamental goals, added significantly into the material behavior, performance limitations, and possible uses of CNP in concrete technology.\u003c/p\u003e \u003cp\u003eCNP's physical, chemical, and mineralogical characterization showed an unusual chemical composition with high levels of tin and antimony and mostly coarse particle size distribution, few levels of reactive oxides like silicon and calcium. These results suggest CNP has little pozzolanic activity and mostly serves as a filler material rather than a geopolymeric cementitious material. With no major reactive elements present, the concrete's pozzolanic reactions help to build its strength and durability by means of which tests of mechanical properties showed clearly lower values for modulus of elasticity, Resistance to tensile cracking, flexural resistance, and resistance to compression when CNP was included. At higher replacement levels, these cuts were especially noticeable. For example, compressive strength fell by 43.8% at 10% CNP replacement; tensile and flexural strengths at 15% CNP replacement dropped by 63% and 59%, respectively. Even though strength numbers dropped with higher CNP content, statistical analysis verified that the observed trends were greatly affected by both replacement level and curing age. These results indicate that although CNP has some negative impact on mechanical performance, appropriate dosing and mix design can help to control the degree of these effects.\u003c/p\u003e \u003cp\u003eDurability tests showed different behavior. While CNP-modified samples showed rising absorption levels with age, control specimens showed predicted declines in water absorption over time. Notably, the 30% CNP blend showed a 58.7% rise in water absorption between 7 and 28 days, which raises questions about its long-term stability especially in moisture-sensitive applications. This implies that increased CNP levels might negatively impact the concrete's resistance to water ingress and related damage mechanisms.\u003c/p\u003e \u003cp\u003eThe research found 10% as the best CNP replacement level despite the constraints. The concrete showed a tolerable drop in mechanical performance at this dosage, yet it preserved same durability qualities as the control. Particularly in light of the possible advantages of using agricultural waste and lowering cement use, this offers a practical compromise between environmental sustainability and performance.\u003c/p\u003e \u003cp\u003eIn conclusion, this study affirms that although CNP cannot completely replace cement without impacting performance, it can be used in small amounts to partly replace cement in non-structural applications. The study stresses the need of cautious material selection, dosage optimization, and performance validation when using alternative binders into cementitious systems. These results meaningfully advance the development of sustainable concrete materials and provide a basis for future inventions in green building technologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eClinical Trial number\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics Approval:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWOA: Conceptualization, Methodology, Supervision,TAW: Investigation, Data curation, Formal analysis, Writing - original draftJIB: Validation, Investigation, Writing - review \u0026amp; editing\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eACI 211.1-91. 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Mechanical Properties and Durability of Sustainable Concrete Containing Various Industrial Solid Wastes. Transportation Research Record, 2675(12), 797-810. https://doi.org/10.1177/03611981211031236 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cashew nut powder, Cement replacement, Sustainable concrete, Mechanical properties, Durability, Agricultural waste valorization","lastPublishedDoi":"10.21203/rs.3.rs-6604841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6604841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the ability of sun-dried cashew-nut powder (CNP) to serve as a renewable partial replacement for cement in concrete manufacturing. The objective of this research is to examine the mechanical properties and longevity of cement-based composites enhanced with different amounts of CNP. Specific objectives are: to define the chemical, mineralogical and physical attributes of CNP, to measure mechanical performance at replacement levels of 0%, 10%, 20%, and 30%, to establish durability indicators and to establish optimum level of CNP replacement. X-ray fluorescence (XRF), particle size distribution, and sieve analysis help define CNP. Cubic specimens with CNP at age twenty-eight and seven days are examined for, flexural resistance, resistance to compression, Stiffness (Elastic Modulus) and resistance to tensile cracking. Accelerated chloride permeability tests and water absorption tests are used to evaluate durability. The mix ratio is optimized and the relationship of CNP content, water-binder ratio, and curing time is studied with the help of response surface methodology. The study aims to ascertain a CNP replacement level that achieves the highest mechanical properties and long-term life with the lowest environmental impact of cement manufacturing. Anticipated outcomes envision CNP as a viable, sustainable cement replacement, thereby enhancing the performance and sustainability of concrete. Especially in regions where cashews are cultivated, this research invites the use of agricultural waste and offers practical guidance for green building practice in Nigeria. It also serves as the foundation for subsequent studies on long-term performance and extended application of CNP-modified concrete for infrastructure development next year.\u003c/p\u003e","manuscriptTitle":"Assessing the Mechanical Properties of Cement-Based Composite Material with Partial Replacement of Cement by Sundried Cashew Nut Powder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 16:12:51","doi":"10.21203/rs.3.rs-6604841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-15T10:07:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T05:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174067431619209767721462671135374830242","date":"2025-06-06T14:49:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T22:47:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"308309828923819014575277781300021933358","date":"2025-06-03T22:39:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283485147475462217408648014064142721219","date":"2025-06-03T16:46:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T15:22:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-03T15:17:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-03T13:15:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-02T18:25:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Civil Engineering","date":"2025-06-02T18:21:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d35db63f-b6ec-4106-affd-9cf04fc69790","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T17:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-05 16:12:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6604841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6604841","identity":"rs-6604841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}