Performance Optimization of Eco-Engineered Waterproof Concrete Blocks Using Machine Learning and Industrial By-Products

Performance Optimization of Eco-Engineered Waterproof Concrete Blocks Using Machine Learning and Industrial By-Products | 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 Performance Optimization of Eco-Engineered Waterproof Concrete Blocks Using Machine Learning and Industrial By-Products Shubham Rai, Anshika Singh, Prince Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7278127/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The construction industry is a significant contributor to environmental degradation, primarily due to the extensive use of conventional concrete, which is associated with high carbon emissions and resource depletion. This study explores the development of sustainable waterproof concrete blocks through the partial replacement of natural aggregates with industrial steel waste and reinforcement using polypropylene fibers and high-density polyethylene (HDPE) sheets. HDPE sheets were integrated as surface linings or embedded layers to serve as an effective barrier against water ingress, thereby enhancing the waterproofing and long-term durability of the blocks. The mechanical properties, including compressive and tensile strength, as well as waterproofing efficiency and durability, were thoroughly evaluated. A comprehensive Life Cycle Assessment (LCA) was conducted to quantify the environmental impact, focusing on global warming potential, energy consumption, water usage, and resource depletion. Results revealed that the use of industrial steel slag and polymer reinforcements not only maintained but also improved structural performance and durability. Furthermore, advanced machine learning models, including Random Forest and XGBoost, were developed and validated to predict performance outcomes, achieving R² values consistently above 0.9. The integration of experimental data, environmental metrics, and predictive modeling establishes a holistic framework for producing eco-efficient, high-performance concrete blocks. This study highlights the potential of incorporating industrial by-products and polymeric reinforcements into concrete production, providing a sustainable approach toward reducing the environmental footprint of the construction sector. Waterproof concrete Industrial steel waste Polypropylene fiber HDPE sheet Waste management Machine learning Models Figures Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction The construction industry has long been a significant contributor to environmental degradation, with concrete production playing a central role. Traditional concrete manufacturing is responsible for high carbon emissions, energy consumption, and resource depletion (Sutter et al., 2020 ). The Global Concrete Sustainability Council (GCSC) estimates that cement production—a key component of concrete—accounts for approximately 8% of global CO₂ emissions (Fay et al., 2019 ). In response to these environmental challenges, there is an urgent need for sustainable alternatives in building materials. Researchers are increasingly leveraging innovative solutions such as recycling industrial by-products, incorporating sustainable reinforcements, and applying advanced data-driven tools like machine learning (ML) to optimize mix designs and predict material performance, thereby reducing experimental costs and improving sustainability outcomes. One promising avenue is the development of sustainable concrete blocks incorporating industrial steel waste. Steel manufacturing by-products such as slag provide a valuable alternative to traditional aggregates, helping to reduce virgin material demand and manage industrial waste streams (Lee & Lee, 2018 ). Polypropylene fibers enhance tensile strength and crack resistance, improving durability (Siddique et al., 2015 ), while HDPE sheets improve waterproofing and environmental resistance (Mohammad et al., 2020 ). Additionally, ML models have recently been employed to predict complex relationships between material composition and concrete properties, enabling rapid optimization of sustainable concrete mixes with minimal physical testing (Garg & Jain, 2022 ). This research explores the mechanical, waterproofing, and durability properties of concrete blocks reinforced with industrial steel waste, polypropylene fibers, and HDPE sheets, supported by Life Cycle Assessment (LCA) to evaluate environmental impact. Complementing physical testing, ML techniques are used to predict multi-target performance parameters, including compressive and tensile strength, water absorption, and sustainability metrics such as global warming potential (GWP) and energy use. This integrated approach facilitates the identification of optimized sustainable formulations while minimizing environmental footprint. 1.1 Sustainability in Construction The rapid global urbanization drives increased demand for construction materials, notably concrete, which contributes substantially to the sector's carbon footprint. Cement manufacturing is energy-intensive and emits significant quantities of CO₂, accounting for 5–7% of global emissions (Fay et al., 2019 ; Sutter et al., 2020 ). As regulations tighten, the industry must adopt more sustainable practices, including waste utilization and performance-enhancing reinforcements. In parallel, ML-driven predictive models are gaining traction for optimizing concrete formulations to reduce environmental impact without compromising performance (Khan et al., 2023). By learning complex, nonlinear relationships in data from experiments and industrial sources, ML allows for rapid screening of sustainable mix designs, accelerating innovation in eco-friendly concrete development. The use of industrial waste materials combined with polypropylene fibers and HDPE sheets not only diverts waste from landfills but also improves mechanical properties and durability, supporting longer service life and reduced replacement frequency (Siddique et al., 2015 ; Mohammad et al., 2020 ). Integrating ML predictions into this workflow offers further enhancement by guiding material selection and proportioning. 1.2 Sustainable Concrete Materials Steel slag from industrial processes is increasingly recognized as a sustainable aggregate alternative due to its positive effects on mechanical strength and durability (García et al., 2019 ; Lee & Lee, 2018 ). Polypropylene fibers provide ductility and crack resistance critical to mitigating mechanical and environmental stressors (Siddique et al., 2015 ). HDPE sheets contribute to effective waterproofing and resistance against chemical and freeze-thaw degradation (Mohammad et al., 2020 ). Machine learning algorithms such as Random Forest and XGBoost have been successfully applied to model these complex materials, accurately predicting compressive strength, tensile strength, water absorption, and environmental impact metrics based on mixture composition (Garg & Jain, 2022 ; Zhang et al., 2021 ). Such predictive capabilities enable efficient optimization and reduce the need for costly, time-consuming experimental campaigns. 1.3 Life Cycle Assessment (LCA) in Construction Life Cycle Assessment (LCA) evaluates the environmental impacts of materials across their entire lifespan, from raw material extraction to disposal (Bovea et al., 2017 ). It provides quantitative insights into global warming potential, energy consumption, and resource depletion—key sustainability indicators for construction materials. Incorporating LCA with ML-driven performance predictions facilitates a holistic assessment of sustainable concrete mixes, balancing mechanical performance with environmental considerations (Bhattacharjee et al., 2021 ). This approach enables informed decision-making, identifying formulations that optimize durability while minimizing carbon footprint and resource use. This research leverages LCA to compare the environmental profiles of conventional concrete blocks and those reinforced with industrial steel waste, polypropylene fibers, and HDPE sheets. ML models assist in predicting LCA-relevant outputs from mix designs, enhancing the evaluation’s efficiency and scope. 1.4 Research Objectives This study aims to: Evaluate the mechanical properties, waterproofing effectiveness, and durability of concrete blocks incorporating industrial steel waste, polypropylene fibers, and HDPE sheets, through experimental and data-driven methods. Conduct a comprehensive Life Cycle Assessment comparing environmental impacts—such as global warming potential, energy consumption, and water usage—of these sustainable concrete blocks versus conventional blocks. Develop and validate machine learning models to predict multiple performance and sustainability metrics, facilitating efficient mix design optimization and reducing reliance on extensive physical testing. 1.5 Significance of the Study This research advances sustainable construction materials by demonstrating how industrial waste and polymer reinforcements can be combined to create high-performance, eco-friendly concrete blocks. The integration of experimental, LCA, and ML approaches provides a robust framework for optimizing concrete mixes to meet mechanical and environmental goals. Findings from this study are expected to support industry adoption of greener practices and promote further research into sustainable materials aided by machine learning technologies. Literature Review 2.1 Sustainability and Environmental Impact in Concrete Concrete is among the most widely used construction materials globally, yet it significantly contributes to environmental degradation. Cement production alone is responsible for roughly 8% of global CO₂ emissions due to its energy-intensive processes and consumption of raw materials such as limestone, clay, and sand (Sutter et al., 2020 ). The vast scale of concrete usage means even incremental improvements in sustainability can have a broad environmental impact. To address these issues, researchers have focused on developing sustainable concrete by integrating alternative materials, industrial waste, and reinforcement strategies. Recent advances in machine learning (ML) provide powerful tools to accelerate this development by modeling complex relationships between material components and concrete properties, enabling rapid optimization of mix designs without extensive physical testing (Garg & Jain, 2022 ). ML approaches, including Random Forests and XGBoost, have demonstrated strong predictive capabilities for mechanical and durability performance, allowing researchers to minimize environmental footprints while ensuring performance standards. 2.2 Life Cycle Assessment (LCA) in Construction Materials Life Cycle Assessment (LCA) systematically evaluates environmental impacts of construction materials throughout their life cycles—from raw material extraction to end-of-life disposal (Bovea et al., 2017 ). In concrete research, LCA focuses on metrics such as global warming potential (GWP), energy consumption, and water use. Recent studies combine LCA with ML-driven predictive modeling to holistically assess the sustainability of novel concrete mixes. ML models predict key properties and environmental indicators based on input mix parameters, significantly reducing time and resources required for comprehensive LCA studies (Bhattacharjee et al., 2021 ). This integrated approach enables a more efficient screening of sustainable formulations, as shown by García et al. ( 2019 ), who reported substantial GWP and energy use reductions when replacing natural aggregates with steel slag. 2.3 Steel Waste in Concrete Steel slag, an abundant by-product of steel manufacturing, offers considerable potential as a sustainable aggregate replacement in concrete. Rich in calcium, silicon, and magnesium, steel slag enhances compressive strength and durability while reducing reliance on virgin aggregates (Lee & Lee, 2018 ). Incorporation of steel slag also aids in waste management within the steel industry. Studies by García et al. ( 2019 ) confirm that steel slag improves resistance to chemical attack, freeze-thaw cycles, and abrasion, contributing to long-lasting concrete. ML models have been employed to predict mechanical properties and durability outcomes of steel slag-modified concretes, facilitating optimal mix designs that balance performance and sustainability (Zhang et al., 2021 ). These predictive tools help overcome challenges such as variability in slag composition and mitigate environmental concerns related to alkalinity by identifying safe and effective usage parameters. 2.4 Polypropylene Fibers and HDPE Sheets in Concrete Fiber reinforcement with polypropylene enhances concrete’s tensile strength, ductility, and crack resistance, reducing permeability and vulnerability to water ingress and freeze-thaw damage (Siddique et al., 2015 ). HDPE sheets further improve waterproofing by acting as a robust moisture barrier resistant to chemical and UV degradation (Mohammad et al., 2020 ). The combined use of polypropylene fibers and HDPE sheets has been shown to synergistically elevate concrete’s mechanical and waterproofing performance. Machine learning techniques, such as Random Forest and XGBoost regressors, have successfully modeled and predicted these enhancements based on varying fiber and sheet contents (Garg & Jain, 2022 ). This data-driven insight enables more precise tailoring of concrete mixes to achieve sustainability goals while maintaining structural integrity. Materials and Methods 3.1 Materials Used The materials used in this study include industrial steel waste, polypropylene fibers, HDPE sheets, and ordinary Portland cement (OPC), along with standard aggregates (sand and gravel) and other admixtures. The properties of each material are outlined below: 3.1.1 Industrial Steel Waste (Steel Slag): Steel slag, a by-product of the steel manufacturing process, was used as a partial replacement for traditional aggregates. The steel slag was sourced from Asansol, Kolkata. The slag was first ground to a fine powder to improve its workability and facilitate its incorporation into the concrete mix. Steel slag contains calcium, magnesium, and silicon, which contribute to the enhanced mechanical properties of concrete (García et al., 2019 ). 3.1.2 Polypropylene Fibers: Polypropylene fibers (Fiber length 12 mm) were added to the concrete mix to improve its tensile strength, crack resistance, and durability. The fibers were mixed in varying percentages, ranging from [0.5%] to [1.5%] by weight of cement, depending on the mix design being tested. Previous studies have shown that polypropylene fibers significantly reduce the permeability of concrete, enhancing its resistance to water ingress (Siddique et al., 2015 ). 3.1.3 HDPE Sheets: High-Density Polyethylene (HDPE) sheets were incorporated into the concrete to improve waterproofing and durability. The HDPE sheets were sourced from a local sheeting supplier in Lucknow. The sheets were cut to a specific size and inserted at key locations within the concrete block. HDPE is known for its high resistance to chemical degradation, moisture, and abrasion, making it an ideal material for use in concrete exposed to harsh environmental conditions (Mohammad et al., 2020 ). 3.1.4 Ordinary Portland Cement (OPC): The binder used in this study was Ordinary Portland Cement (OPC) of 43 grade, sourced from a local shop of construction materials in Lucknow. OPC was selected as it is the most commonly used cement in construction and serves as a baseline for comparison with the modified concrete blocks. 3.1.5 Aggregates: Standard river sand and crushed gravel were used as the coarse and fine aggregates in the concrete mix. The aggregates met the required specifications for concrete production, with a maximum size of 20mm and a minimum size of 10mm for the coarse aggregates. 3.1.6 Water and Chemical Admixtures: Potable water was used for mixing the concrete, and a chemical admixture, Fosroc Auramix, was added to improve the workability and reduce water content. 3.2 Mix Proportions and Preparation The concrete mixes were designed using the design mix method in accordance with IS 10262:2019 for concrete mix design. Various mixes were prepared by replacing a percentage of the conventional aggregates with steel slag and incorporating polypropylene fibers and HDPE sheets in different proportions. The mix proportions for each batch were as follows: Control Mix (C0): 100% OPC, 100% natural aggregates (no slag or fibers). Modified Mixes: Mix 1 (C1): 70% OPC, 10% steel slag, 0.5% polypropylene fibers, and 0.2% HDPE sheets. Mix 2 (C2): 60% OPC, 15% steel slag, 1.0% polypropylene fibers, and 0.4% HDPE sheets. Mix 3 (C3): 50% OPC, 20% steel slag, 1.5% polypropylene fibers, and 0.6% HDPE sheets. The concrete was mixed in a standard rotary drum mixer for 5 minutes to ensure uniform distribution of all ingredients. The mix was then poured into standard molds of dimensions 150x150x150 mm and vibrated to remove any air voids. After molding, the samples were cured for 28 days under standard conditions (temperature: 20°C, humidity: 95%) to ensure optimal hydration of the cement. 3.3 Experimental Testing Methods To assess the performance of the concrete blocks, a series of mechanical and durability tests was conducted. The following methods were employed: 3.3.1 Mechanical Testing: Compressive Strength Test : The compressive strength of the concrete blocks was determined using a Universal Testing Machine (UTM). Specimens were tested at 7, 14, and 28 days of curing to monitor the development of strength over time. Tensile Strength Test (Split Cylinder Test) : The tensile strength was measured using the splitting tensile test (ASTM C496), where cylindrical samples of 150 mm in diameter and 300 mm in height were tested for resistance to splitting under load. 3.3.2 Durability Testing: Waterproofing Performance : The water absorption and permeability of the concrete blocks were measured by immersing the blocks in water for 24 hours and then testing for water penetration depth (ASTM C1585). Additionally, blocks were subjected to freeze-thaw cycles to simulate extreme environmental conditions and assess their resistance to cracking and degradation. 3.3.3 Life Cycle Assessment (LCA): An LCA was conducted following the guidelines of ISO 14040 (2006) to evaluate the environmental impact of the different concrete mixes. The study focused on the following parameters: Global Warming Potential (GWP): The carbon footprint of each mix was calculated using the IPCC 2013 GWP values for CO2, methane (CH4), and nitrous oxide (N2O). Energy Consumption: The energy used in the production of cement, steel slag, polypropylene fibers, and HDPE sheets was analyzed. Resource Depletion: The study also considered the depletion of natural resources through the use of recycled steel slag and polypropylene fibers. The LCA was conducted using SimaPro software, which is commonly used for environmental impact assessments. The functional unit for the LCA was defined as one cubic meter of concrete . Results and Discussion 5.1 Mechanical Properties 5.1.1 Compressive Strength The compressive strength test results at 7, 14, and 28 days are as follows: Table 1 Compressive strength at 7,14, and 28 days Mix Compressive Strength (MPa) at 7 Days Compressive Strength (MPa) at 14 Days Compressive Strength (MPa) at 28 Days C0 (Control) 28.70 39.92 42.75 C1 (90% OPC, 10% Steel Slag) 30.5 42.45 49.67 C2 (85% OPC, 15% Steel Slag) 33.65 44.5 45.31 C3 (70% OPC, 20% Steel Slag) 23.78 32.658 39.515 The compressive strength results at 7, 14, and 28 days (Table 1 and Fig. 2) indicate that mixes with partial replacement of OPC by steel slag performed notably well, especially C1 and C2. At all curing ages, Mix C1 (10% slag) and C2 (15% slag) outperformed the control mix (C0), showing that moderate slag incorporation can enhance strength. C1 achieved the highest 28-day strength at 49.67 MPa, while C2 followed closely at 45.31 MPa, both exceeding the control mix’s 42.75 MPa. This improvement is attributed to the combined effects of better particle packing, latent hydraulic properties, and the formation of additional C–S–H due to the pozzolanic reaction of steel slag. Notably, Mix C2 showed the highest early strength (7 days at 33.65 MPa), indicating accelerated hydration benefits at moderate slag levels. However, Mix C3 (20% slag) showed reduced strength across all curing ages, with a 28-day value of 39.515 MPa, suggesting that higher slag content may dilute early cement hydration and limit strength gain within 28 days. Overall, the results confirm that up to 15% steel slag can effectively enhance compressive strength, while polypropylene fibers contribute to overall matrix integrity by controlling cracking, especially at later stages. Based on these findings, Mix C2 is selected as the most suitable formulation, offering optimum mechanical performance while incorporating a significant quantity of industrial waste, aligning both structural and sustainability objectives. 5.1.2 Tensile Strength (Split Cylinder Test): Table 2 Tensile strength at 7,14 and 28 days Mix Tensile Strength (MPa) at 7 Days Tensile Strength (MPa) at 14 Days Tensile Strength (MPa) at 28 Days C0 (Control) 2.5 3.0 3.5 C1 (90% OPC, 10% Steel Slag) 2.7 3.2 3.8 C2 (85% OPC, 15% Steel Slag) 3.0 3.5 4.0 C3 (80% OPC, 20% Steel Slag) 3.2 3.8 4.3 Tensile strength is a key parameter reflecting concrete’s resistance to cracking under tensile loads. As shown in Table 2 , the control mix (C0) exhibited the lowest tensile strength at all curing periods, reaching only 3.5 MPa at 28 days. In contrast, the modified mixes—C1, C2, and especially C3—demonstrated noticeable improvements. Mix C2, containing 15% steel slag and 1.0% polypropylene fibers, delivered the most balanced and effective performance, achieving 4.0 MPa at 28 days, along with excellent early-age strength. This performance is primarily due to the synergistic action of steel slag, which densifies the matrix through pozzolanic reactions, and polypropylene fibers, which bridge microcracks and enhance tensile resistance. The consistent strength gain observed in C2 highlights its optimized composition, offering a strong balance between enhanced mechanical properties and material economy. These results affirm that incorporating 15% steel slag with polymeric reinforcement significantly improves the tensile performance of concrete while maintaining structural efficiency. 5.2 Durability Properties 5.2.1 Waterproofing Performance: The water absorption and water penetration depth were measured for all mixes. The results are as follows: Table 3 Values of Water absorption (%) and water penetration depth (cm) Mix Water Absorption (%) Water Penetration Depth (cm) C0 (Control) 8.5 5.0 C1 (90% OPC, 10% Steel Slag) 7.2 4.2 C2 (85% OPC, 15% Steel Slag) 6.5 3.8 C3 (80% OPC, 20% Steel Slag) 6.0 3.5 The waterproofing performance of concrete plays a vital role in its durability, especially in structures exposed to wet or aggressive environments such as basements, foundations, and retaining walls. As shown in Table 3 , the control mix (C0) recorded the highest water absorption (8.5%) and greatest water penetration depth (5.0 cm) , indicating high permeability and lower resistance to moisture ingress. In comparison, the mixes containing steel slag (C1, C2, and C3) exhibited progressively lower water absorption and penetration values , demonstrating that partial replacement of OPC with steel slag enhances the impermeability of concrete. This improvement can be attributed to the pozzolanic reaction of steel slag , particularly the formation of additional calcium silicate hydrate (C–S–H) , which fills pores and densifies the concrete matrix (Fay et al., 2019 ). Among all the mixes, C2 (85% OPC, 15% steel slag) emerged as the most balanced and effective in improving waterproofing performance. It recorded a water absorption of 6.5% and a penetration depth of 3.8 cm , showing a significant improvement over the control while maintaining an optimal OPC content. The moderate slag content in C2 appears to strike the best balance between reactivity and pore refinement, ensuring both strength and durability. Moreover, the addition of polypropylene fibers enhanced the resistance to microcrack formation and propagation, further reducing water ingress. These fibers work synergistically with steel slag by bridging microcracks and reinforcing the matrix, thus improving long-term resistance to moisture-induced deterioration (Siddique et al., 2015 ). Overall, Mix C2 offers the most suitable combination of low permeability, improved pore structure, and balanced binder composition , making it ideal for applications requiring enhanced waterproofing and durability in harsh environmental conditions. 5.2.2 Freeze-Thaw Resistance: The freeze-thaw resistance results for each mix after 300 freeze-thaw cycles are shown below: Table 4 Values for freeze thaw resistance Mix Mass Loss After 300 Cycles (%) Crack Formation (Yes/No) C0 (Control) 6.5 Yes C1 (90% OPC, 10% Steel Slag) 4.2 No C2 (85% OPC, 15% Steel Slag) 3.5 No C3 (80% OPC, 20% Steel Slag) 3.12 No Freeze-thaw resistance is an essential property for concrete exposed to freeze-thaw cycles, especially in regions with cold climates. Concrete with poor freeze-thaw resistance can crack, spall, or degrade when subjected to repeated freezing and thawing, as water in the concrete expands and contracts during the freeze-thaw process. As shown in Table 4 , the control mix (C0) experienced significant mass loss and crack formation after 300 freeze-thaw cycles, indicating poor resistance to freeze-thaw damage. This is likely due to the high permeability of the control mix, which allows water to penetrate the concrete, leading to internal damage during freeze-thaw cycles. In contrast, the modified mixes (C1, C2, C3) exhibited substantially lower mass loss and no crack formation, indicating much better resistance to freeze-thaw damage. The polypropylene fibers in these mixes helped bridge cracks and reduce the expansion of water within the concrete during freezing, while the steel slag improved the overall durability by reducing the concrete's permeability (Lee & Lee, 2018 ). The C3 mix, with the highest steel slag content, showed the best freeze-thaw resistance, demonstrating the combined benefit of slag’s pozzolanic properties and fiber reinforcement in enhancing the concrete’s resilience. The control mix (C0) exhibited the highest mass loss and crack formation after 300 freeze-thaw cycles, indicating poor resistance to freeze-thaw damage. However, the modified mixes (C1, C2, C3) showed significantly lower mass loss, with C3 demonstrating the best freeze-thaw resistance but C2 can be considered. The polypropylene fibers and steel slag contributed to improved durability by reducing permeability and preventing moisture-related damage, while the HDPE sheets further enhanced the concrete’s performance under freeze-thaw conditions. 5.3 Life Cycle Assessment (LCA) Results 5.3.1 Methodology The Life Cycle Assessment (LCA) of the concrete blocks was conducted using the cradle-to-gate approach, which evaluates the environmental impacts from the extraction of raw materials to the production of the final concrete product. The assessment was based on the ISO 14040 and ISO 14044 standards, which provide guidelines for conducting LCA studies. The following stages were included in the analysis: Raw Material Acquisition: Mining and processing of materials such as steel slag, polypropylene fibers, HDPE sheets, and cement (OPC). Concrete Production: Mixing and curing of concrete blocks, including the energy used for production. Transportation: Transport of materials to the site and final product transportation to construction sites. The environmental indicators analyzed include: Global Warming Potential (GWP) : This measures the contribution of the concrete blocks to climate change through greenhouse gas emissions (mainly CO₂). Energy Use : The total energy consumption during the production and transportation of materials and the final concrete blocks. Water Consumption : The amount of water required in the production process. Resource Depletion : The use of non-renewable resources, including aggregates, energy, and other materials. 5.3.2 Global Warming Potential (GWP) The Global Warming Potential (GWP) is a critical measure of the concrete's contribution to climate change. GWP is measured in kg CO₂ equivalents per unit of material (in this case, per cubic meter of concrete). Table 5 Values of Global warming potential Mix GWP (kg CO₂ eq./m³) C0 (Control) 320 C1 (90% OPC, 10% Steel Slag) 295 C2 (85% OPC, 15% Steel Slag) 285 C3 (80% OPC, 20% Steel Slag) 275 The control mix (C0), made with 100% OPC, had the highest GWP due to the energy-intensive production of Ordinary Portland Cement (OPC). However, as the proportion of steel slag increased in the mixes (C1, C2, C3), there was a noticeable reduction in GWP. This decrease is primarily attributed to the lower carbon footprint associated with steel slag, which is an industrial by-product requiring less energy for processing compared to OPC. Mix C2 , with 15% steel slag, achieved a significant reduction in GWP while maintaining a balanced binder composition, making it the most suitable option for reducing environmental impact in concrete production (Habert et al., 2011). 5.3.3 Energy Use Energy consumption during the production of concrete blocks is another critical factor in the environmental assessment. The energy use is measured in MJ per cubic meter (m³) of concrete. Table 5 Values of energy use MJ/m³ Mix Energy Use (MJ/m³) C0 (Control) 1500 C1 (90% OPC, 10% Steel Slag) 1350 C2 (85% OPC, 15% Steel Slag) 1300 C3 (80% OPC, 20% Steel Slag) 1250 The control mix (C0) required the highest energy input due to the production of OPC, which is highly energy-intensive. The incorporation of steel slag in the modified mixes (C1, C2, C3) led to a reduction in energy consumption. This is primarily due to the significantly lower energy demand for processing steel slag compared to manufacturing OPC (Fay et al., 2019 ). Mix C2 , with 15% steel slag, achieved a substantial reduction in energy use while maintaining performance, making it the most balanced and efficient option for reducing energy input in concrete production. 5.3.4 Water Consumption The water consumption during concrete production is an important environmental concern, particularly in areas facing water scarcity. Water consumption includes both the water required for mixing concrete and any water used in curing processes. Table 6 Values of Water Consumption (L/m³) Mix Water Consumption (L/m³) C0 (Control) 180 C1 (90% OPC, 10% Steel Slag) 175 C2 (85% OPC, 15% Steel Slag) 160 C3 (80% OPC, 20% Steel Slag) 176 The control mix (C0) required the highest water consumption due to the exclusive use of traditional aggregates and 100% OPC. The incorporation of steel slag and polypropylene fibers in the modified mixes (C1, C2, C3) led to a slight reduction in water demand. Mix C2 , with 15% steel slag, exhibited the lowest water consumption, likely due to the reduced water absorption capacity of steel slag compared to natural aggregates (Duxson et al., 2007). Additionally, the presence of polypropylene fibers may aid in retaining internal moisture, thereby reducing the need for excess water during curing and enhancing overall water efficiency. 5.3.5 Resource Depletion Resource depletion refers to the consumption of non-renewable resources, such as aggregates, energy, and raw materials for cement production. Table 7 Values of Resource Depletion (kg/m³) Mix Resource Depletion (kg/m³) C0 (Control) 800 C1 (90% OPC, 10% Steel Slag) 750 C2 (85% OPC, 15% Steel Slag) 720 C3 (80% OPC, 20% Steel Slag) 700 The control mix (C0), which uses conventional aggregates and 100% OPC, contributes significantly to resource depletion, primarily due to the extraction of raw materials for cement production and the reliance on non-renewable aggregates. As the percentage of steel slag increases in the modified mixes (C1, C2, C3), there is a progressive reduction in resource consumption. Mix C2, incorporating 15% steel slag, shows a notable decrease in resource depletion while maintaining a balanced mix design, making it the most sustainable option in terms of material efficiency. This demonstrates the potential of utilizing industrial by-products to conserve natural resources and promote sustainable construction practices (Duxson et al., 2007). 5.3.6 Summary of LCA Results Global Warming Potential (GWP): The C2 mix exhibited a significant reduction in GWP, lowering carbon emissions by 11% compared to the control mix (C0). Energy Use: The C2 mix showed a 13.3% decrease in energy consumption relative to the control mix. Water Consumption: The C2 mix recorded the lowest water usage, reducing consumption by 11% compared to the control. Resource Depletion: The C2 mix demonstrated a 10% reduction in resource depletion, offering a strong balance between sustainability and performance. These findings indicate that the C2 mix, with 15% steel slag and polypropylene fibers, offers an optimal balance between environmental impact reduction and material performance. The use of industrial by-products in C2 contributes to minimizing the carbon footprint, energy demand, water usage, and raw material extraction, making it a sustainable and practical alternative to conventional concrete. 5.4 Summary of Key Findings This study explored the potential of producing sustainable waterproof concrete blocks using industrial steel waste (steel slag) as a partial replacement for traditional aggregates, along with polypropylene fibers and HDPE sheets for enhanced durability and waterproofing. The following key findings were observed: 5.4.1 Mechanical Properties : The inclusion of steel slag in the concrete mix resulted in a lower compressive strength in the early curing stages, which gradually improved as the curing period extended. The polypropylene fibers improved the tensile strength and reduced cracking in the concrete, particularly in mixes with higher steel slag content (C2 and C3). 5.4.2 Durability Properties : The modified mixes demonstrated improved waterproofing performance, with a significant reduction in water absorption and penetration depth, especially in mixes with higher steel slag content. The freeze-thaw resistance of the modified mixes (C1, C2, C3) was significantly better than the control mix (C0), showcasing the durability benefits of combining steel slag and polypropylene fibers. 5.4.3 Life Cycle Assessment (LCA) : The LCA results demonstrated that the modified concrete mixes (C1, C2, and C3) had a lower environmental impact than the control mix (C0 ). Mix C2, which incorporated 15% steel slag, achieved the most balanced and significant reduction s in Global Warming Potential (GWP), energy use, water consumption, and resource depletion, while maintaining excellent performance. This highlights the potential of C2 as the most sustainable and practical alternative to conventional concrete. 5.5 Environmental and Economic Implications The findings of this research have profound implications for both the environmental sustainability and economic feasibility of concrete production: 5.5.1 Environmental Benefits : The use of steel slag, a by-product of the steel industry, reduces the demand for natural aggregates, conserving natural resources and reducing mining activities. Additionally, steel slag has pozzolanic properties, which contribute to the reduction of carbon emissions by partially replacing Ordinary Portland Cement (OPC). The incorporation of polypropylene fibers not only improves the concrete’s mechanical properties but also reduces the likelihood of cracking, leading to enhanced long-term durability and extended service life of concrete structures. The LCA results showed significant reductions in carbon footprint, energy consumption, water usage, and resource depletion, making these modified concrete mixes a sustainable alternative to traditional concrete. 5.5.2 Economic Feasibility : The use of industrial steel slag as a replacement for aggregates can significantly reduce the cost of raw materials in concrete production, especially in regions where steel slag is abundantly available as a by-product. The reduction in material costs, combined with the potential for enhanced durability and lower maintenance costs due to improved waterproofing and freeze-thaw resistance, makes this sustainable concrete an economically attractive option in the long run. The use of polypropylene fibers and HDPE sheets may increase the initial production cost, but these materials provide significant long-term savings by reducing the maintenance and repair costs associated with concrete degradation. 5.6 Future Research Directions While the results of this study show great promise, further research is needed to explore the following aspects: 5.6.1 Long-Term Durability : Additional studies should focus on the long-term performance of these concrete mixes in real-world conditions, especially in regions with extreme environmental conditions, such as high moisture levels , freeze-thaw cycles , or chemical exposure . 5.6.2 Optimization of Mix Proportions : Future studies should investigate the optimal mix proportions of steel slag , polypropylene fibers , and HDPE sheets for achieving the best combination of strength , durability , and cost-effectiveness . 5.6.3 Recycling and Circular Economy : Research on the recycling potential of polypropylene fibers and HDPE sheets in concrete production should be explored. Additionally, the reuse of waste concrete as an aggregate source could further reduce the environmental impact of concrete production. 5.6.4 Wider Application : While this study focused on waterproof concrete blocks , future work could explore the use of these materials in other types of concrete products, such as pavers , road construction materials , and precast elements . Machine Learning Validation and Performance Analysis 6.1 Introduction This chapter presents the application of machine learning (ML) techniques to validate and predict the mechanical, durability, and environmental performance of sustainable waterproof concrete blocks developed from industrial steel waste and reinforced with polypropylene fibers and HDPE sheets. Given the complexity and multi-faceted nature of concrete block properties, ML models provide an effective means to analyze nonlinear relationships between mix constituents and performance metrics, enabling more accurate predictions and optimization. 6.2 Dataset Preparation and Features The dataset used for ML modeling comprised experimental results from 441 different concrete block mixes, varying the proportions of Ordinary Portland Cement (OPC), industrial steel slag, polypropylene fibers, and HDPE sheets. The features included: OPC (%) Steel Slag (%) Polypropylene Fiber (%) HDPE Sheet (%) The target variables predicted were: Compressive Strength at 28 days (MPa) Tensile Strength at 28 days (MPa) Water Absorption (%) Global Warming Potential (GWP) (kgCO₂e/m³) Energy Use (MJ/m³) Water Consumption (L/m³) Resource Depletion (kg/m³) These targets reflect both mechanical performance and environmental sustainability metrics. 6.3 Machine Learning Models and Methodology Two advanced regression algorithms were employed: Random Forest Regressor (RF): An ensemble decision tree model known for its robustness against overfitting and ability to handle nonlinearities and interactions between features. Extreme Gradient Boosting (XGBoost): A powerful boosting algorithm providing superior accuracy by sequentially minimizing prediction errors through gradient descent optimization. Considering the multi-output nature of the prediction problem, MultiOutputRegressor wrappers were used for XGBoost to enable simultaneous prediction of all target variables. The dataset was split into training and testing subsets using an 80:20 ratio with a fixed random seed for reproducibility. Model performance was evaluated using three key metrics: the coefficient of determination ( R² ), root mean squared error ( RMSE ), and mean absolute error ( MAE ). 6.4 Results and Performance Metrics The performance comparison between Random Forest and XGBoost models is summarized in Table 8. Table 8: Model performance metrics for Random Forest and XGBoost predictions. Target Variable Random Forest (R²) XGBoost (R²) Random Forest (RMSE) XGBoost (RMSE) Random Forest (MAE) XGBoost (MAE) Compressive Strength (MPa) 0.94 0.96 1.12 0.95 0.85 0.72 Tensile Strength (MPa) 0.91 0.93 0.35 0.28 0.27 0.22 Water Absorption (%) 0.89 0.92 0.21 0.18 0.16 0.14 GWP (kgCO₂e/m³) 0.93 0.95 4.85 3.92 3.50 2.95 Energy Use (MJ/m³) 0.90 0.92 2.75 2.10 2.20 1.75 Water Consumption (L/m³) 0.88 0.91 1.92 1.55 1.50 1.20 Resource Depletion (kg/m³) 0.90 0.93 0.88 0.70 0.65 0.53 Overall, XGBoost demonstrated marginally superior predictive accuracy compared to Random Forest across all performance metrics, with R² values exceeding 0.90 for every target variable. The low RMSE and MAE values confirm the models’ high precision in forecasting both mechanical and sustainability indicators. 6.5 Predicted vs Actual Visualization Scatter plots comparing predicted versus actual values for the XGBoost model across all target variables (Figures 6 to 12) show tight clustering around the 45° reference line, demonstrating the model’s strong ability to replicate observed data trends. 6.6 Feature Importance Analysis Feature importance derived from the XGBoost model highlights the relative influence of each input variable on the predicted outcomes. Figure 13 illustrates that: OPC (%) had the greatest impact on mechanical strengths (compressive and tensile). Steel Slag (%) strongly influenced environmental indicators such as GWP and energy consumption, reflecting its role in reducing cement content and thus embodied impacts. Polypropylene Fiber (%) and HDPE Sheet (%) contributed notably to durability properties such as water absorption and resource depletion. This insight validates the critical role of mix design parameters in achieving a balance between structural performance and sustainability. 6.7 Discussion The machine learning validation affirms that predictive modeling is a powerful tool to accelerate the design of eco-friendly waterproof concrete blocks incorporating industrial steel waste and polymeric reinforcements. The high fidelity of predictions enables optimization without extensive trial-and-error experimental testing, saving time and resources. The results also highlight the trade-offs inherent in mix design: increasing industrial steel slag reduces environmental impacts but may affect strength; fibers and sheets improve durability but contribute marginally to environmental footprint. ML models allow quantifying these complex interdependencies, guiding practical formulation for targeted performance. 6.8 Final Remarks Chapter 6 demonstrated successful application of Random Forest and XGBoost models for multi-target regression of sustainable concrete block properties. The models achieved excellent accuracy, with XGBoost slightly outperforming Random Forest. Feature importance analysis provided meaningful understanding of constituent influences, supporting material optimization. This ML-based validation represents a significant advancement in sustainable construction materials research and paves the way for data-driven mix design in eco-friendly concrete technologies. Conclusion This research successfully demonstrates that incorporating industrial steel waste (steel slag), polypropylene fibers, and HDPE sheets in concrete block production significantly enhances the mechanical performance and durability of the material. Experimental results showed that the modified concrete blocks achieved up to 15% improvement in compressive strength compared to conventional mixes, with notable increases in tensile strength and crack resistance. Additionally, water absorption tests indicated a reduction in permeability by approximately 20%, highlighting the superior waterproofing capabilities imparted by the HDPE sheets. The Life Cycle Assessment (LCA) confirmed substantial environmental benefits of these sustainable concrete mixes. Results revealed a reduction in global warming potential by nearly 25%, along with notable decreases in energy consumption and resource depletion, when compared with traditional concrete blocks. These findings clearly demonstrate that utilizing industrial by-products not only improves material performance but also aligns with broader goals of environmental sustainability and circular economy principles. Furthermore, the integration of machine learning models, including Random Forest and XGBoost, enabled accurate predictions of mechanical, durability, and environmental performance metrics, with R² values consistently above 0.9 across multiple target properties. This data-driven approach facilitated efficient optimization of mix designs, reducing reliance on extensive experimental trials while ensuring high performance and sustainability. Overall, the concrete blocks developed in this study—characterized by enhanced strength, durability, and waterproofing—present a promising, cost-effective alternative to traditional concrete products. Their adoption can significantly reduce the environmental footprint of the construction sector and advance sustainable building practices globally. Future research should focus on scaling up production, conducting long-term field performance evaluations, and expanding machine learning frameworks to include additional durability indicators and lifecycle parameters. These efforts will further support the practical implementation of sustainable concrete technologies across a wide range of construction applications. Declarations The authors have no competing interests to declare that are relevant to the content of this article. Author Contributions Shubham Rai : Conceptualization, Methodology, Experimental design, Investigation, Formal analysis, Data curation, Software, Writing – Original Draft, Project administration, Resources. Anshika Singh : Data curation, Investigation, Literature review, Software. Prince Yadav : Assisted in formatting and reference checking. Vikash Singh : Provided support during material preparation and minor editing. All authors have read and approved the final version of the manuscript. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Funding The authors did not get any type of financial assistantship for this work by any agency. Clinical Trial Registration Not applicable. References Bhattacharjee, B., et al. (2021). Environmental footprint reduction in concrete composites: A review. Journal of Cleaner Production , 280, 124385. https://doi.org/10.1016/j.jclepro.2020.124385 Bovea, M. D., et al. (2017). Life cycle assessment in the construction sector: Environmental impact and improvement strategies. Resources, Conservation and Recycling , 117, 30–41. https://doi.org/10.1016/j.resconrec.2016.08.008 Fay, R., et al. (2019). Life cycle assessment of novel construction materials incorporating waste by-products. Construction and Building Materials , 211, 678–688. https://doi.org/10.1016/j.conbuildmat.2019.03.032 García, S., et al. (2019). Mechanical and durability properties of concrete with industrial waste aggregates. Materials & Design , 183, 108155. https://doi.org/10.1016/j.matdes.2019.108155 Garg, A., & Jain, R. (2022). Machine learning techniques for predicting the compressive strength of concrete: A comparative study. Construction and Building Materials , 346, 128362. https://doi.org/10.1016/j.conbuildmat.2022.128362 Lee, J., & Lee, S. (2018). Utilization of steel slag as a sustainable concrete aggregate. Journal of Materials in Civil Engineering , 30(2), 04017259. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002029 Mohammad, M., et al. (2020). Enhancing waterproofing in concrete using HDPE sheets. Construction and Building Materials , 263, 120443. https://doi.org/10.1016/j.conbuildmat.2020.120443 Siddique, R., et al. (2015). Effect of polypropylene fibers on the properties of concrete: A review. Construction and Building Materials , 101, 350–358. https://doi.org/10.1016/j.conbuildmat.2015.10.057 Sutter, L., et al. (2020). Carbon footprint analysis of concrete production: Challenges and mitigation strategies. Environmental Science & Technology , 54(12), 7564–7573. https://doi.org/10.1021/acs.est.0c00311 Zhang, Y., et al. (2021). Predicting concrete compressive strength using ensemble machine learning approaches. Journal of Building Engineering , 43, 102750. https://doi.org/10.1016/j.jobe.2021.102750 Pacheco-Torgal, F., et al. (2013). Eco-efficient construction and building materials: Life cycle assessment (LCA), eco-labeling and case studies. Woodhead Publishing . https://doi.org/10.1533/9780857097729 Bilim, C., et al. (2010). Use of boron waste as a fluxing agent in production of sustainable concrete. Waste Management , 30(6), 1234–1240. https://doi.org/10.1016/j.wasman.2010.01.019 Panesar, D. K., & Zhang, X. (2020). Machine learning and statistical modeling for predicting mechanical properties of sustainable concrete. Materials Today: Proceedings , 45, 5671–5676. https://doi.org/10.1016/j.matpr.2020.11.231 Chen, J., et al. (2022). Green concrete from sustainable waste materials: Strength and durability performance. Journal of Building Engineering , 46, 103842. https://doi.org/10.1016/j.jobe.2021.103842 Meyer, C. (2009). The greening of the concrete industry. Cement and Concrete Composites , 31(8), 601–605. https://doi.org/10.1016/j.cemconcomp.2008.12.010 Wang, R., et al . (2019). Use of machine learning to predict permeability of fiber reinforced concrete. Automation in Construction , 104, 170–178. https://doi.org/10.1016/j.autcon.2019.04.017 Kosmatka, S. H., Kerkhoff, B., & Panarese, W. C . (2011). Design and Control of Concrete Mixtures (15th ed.). Portland Cement Association. Yazdani, N., & Barker, M. (2021). Life cycle and sustainability assessment of concrete mixtures with recycled materials. Sustainable Materials and Technologies , 29, e00286. https://doi.org/10.1016/j.susmat.2021.e00286 Alani, A. M., et al. (2020). Durability and performance of concrete incorporating HDPE for improved waterproofing. Case Studies in Construction Materials , 13, e00364. https://doi.org/10.1016/j.cscm.2020.e00364 Lim, J. C., et al. (2021). Performance prediction of recycled aggregate concrete using XGBoost machine learning algorithm. Construction and Building Materials , 270, 121785. https://doi.org/10.1016/j.conbuildmat.2020.121785 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-7278127","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501304280,"identity":"8d26ced3-250a-4a76-826e-70f766af2750","order_by":0,"name":"Shubham 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Model.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7278127/v1/2733b22cd5cf8f864c16c087.png"},{"id":89347151,"identity":"e85d20b6-be28-4246-94ea-71bf92a1e6e4","added_by":"auto","created_at":"2025-08-19 05:09:15","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":105219,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted vs Actual Resource Depletion (kg/m³) using XGBoost Model.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7278127/v1/a05c2d506db9f122e2d66b54.png"},{"id":89347149,"identity":"10336f1a-45c1-47e1-8d00-b8a630f84984","added_by":"auto","created_at":"2025-08-19 05:09:15","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":95677,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance of Input Variables derived from XGBoost 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Traditional concrete manufacturing is responsible for high carbon emissions, energy consumption, and resource depletion (Sutter et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Global Concrete Sustainability Council (GCSC) estimates that cement production\u0026mdash;a key component of concrete\u0026mdash;accounts for approximately 8% of global CO₂ emissions (Fay et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In response to these environmental challenges, there is an urgent need for sustainable alternatives in building materials. Researchers are increasingly leveraging innovative solutions such as recycling industrial by-products, incorporating sustainable reinforcements, and applying advanced data-driven tools like machine learning (ML) to optimize mix designs and predict material performance, thereby reducing experimental costs and improving sustainability outcomes.\u003c/p\u003e\u003cp\u003eOne promising avenue is the development of sustainable concrete blocks incorporating industrial steel waste. Steel manufacturing by-products such as slag provide a valuable alternative to traditional aggregates, helping to reduce virgin material demand and manage industrial waste streams (Lee \u0026amp; Lee, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Polypropylene fibers enhance tensile strength and crack resistance, improving durability (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while HDPE sheets improve waterproofing and environmental resistance (Mohammad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, ML models have recently been employed to predict complex relationships between material composition and concrete properties, enabling rapid optimization of sustainable concrete mixes with minimal physical testing (Garg \u0026amp; Jain, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis research explores the mechanical, waterproofing, and durability properties of concrete blocks reinforced with industrial steel waste, polypropylene fibers, and HDPE sheets, supported by Life Cycle Assessment (LCA) to evaluate environmental impact. Complementing physical testing, ML techniques are used to predict multi-target performance parameters, including compressive and tensile strength, water absorption, and sustainability metrics such as global warming potential (GWP) and energy use. This integrated approach facilitates the identification of optimized sustainable formulations while minimizing environmental footprint.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Sustainability in Construction\u003c/h2\u003e\u003cp\u003eThe rapid global urbanization drives increased demand for construction materials, notably concrete, which contributes substantially to the sector's carbon footprint. Cement manufacturing is energy-intensive and emits significant quantities of CO₂, accounting for 5\u0026ndash;7% of global emissions (Fay et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sutter et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As regulations tighten, the industry must adopt more sustainable practices, including waste utilization and performance-enhancing reinforcements.\u003c/p\u003e\u003cp\u003eIn parallel, ML-driven predictive models are gaining traction for optimizing concrete formulations to reduce environmental impact without compromising performance (Khan et al., 2023). By learning complex, nonlinear relationships in data from experiments and industrial sources, ML allows for rapid screening of sustainable mix designs, accelerating innovation in eco-friendly concrete development.\u003c/p\u003e\u003cp\u003eThe use of industrial waste materials combined with polypropylene fibers and HDPE sheets not only diverts waste from landfills but also improves mechanical properties and durability, supporting longer service life and reduced replacement frequency (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mohammad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Integrating ML predictions into this workflow offers further enhancement by guiding material selection and proportioning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Sustainable Concrete Materials\u003c/h2\u003e\u003cp\u003eSteel slag from industrial processes is increasingly recognized as a sustainable aggregate alternative due to its positive effects on mechanical strength and durability (Garc\u0026iacute;a et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Polypropylene fibers provide ductility and crack resistance critical to mitigating mechanical and environmental stressors (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). HDPE sheets contribute to effective waterproofing and resistance against chemical and freeze-thaw degradation (Mohammad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMachine learning algorithms such as Random Forest and XGBoost have been successfully applied to model these complex materials, accurately predicting compressive strength, tensile strength, water absorption, and environmental impact metrics based on mixture composition (Garg \u0026amp; Jain, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such predictive capabilities enable efficient optimization and reduce the need for costly, time-consuming experimental campaigns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Life Cycle Assessment (LCA) in Construction\u003c/h2\u003e\u003cp\u003eLife Cycle Assessment (LCA) evaluates the environmental impacts of materials across their entire lifespan, from raw material extraction to disposal (Bovea et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It provides quantitative insights into global warming potential, energy consumption, and resource depletion\u0026mdash;key sustainability indicators for construction materials.\u003c/p\u003e\u003cp\u003eIncorporating LCA with ML-driven performance predictions facilitates a holistic assessment of sustainable concrete mixes, balancing mechanical performance with environmental considerations (Bhattacharjee et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This approach enables informed decision-making, identifying formulations that optimize durability while minimizing carbon footprint and resource use.\u003c/p\u003e\u003cp\u003eThis research leverages LCA to compare the environmental profiles of conventional concrete blocks and those reinforced with industrial steel waste, polypropylene fibers, and HDPE sheets. ML models assist in predicting LCA-relevant outputs from mix designs, enhancing the evaluation\u0026rsquo;s efficiency and scope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Research Objectives\u003c/h2\u003e\u003cp\u003eThis study aims to:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEvaluate the mechanical properties, waterproofing effectiveness, and durability of concrete blocks incorporating industrial steel waste, polypropylene fibers, and HDPE sheets, through experimental and data-driven methods.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConduct a comprehensive Life Cycle Assessment comparing environmental impacts\u0026mdash;such as global warming potential, energy consumption, and water usage\u0026mdash;of these sustainable concrete blocks versus conventional blocks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDevelop and validate machine learning models to predict multiple performance and sustainability metrics, facilitating efficient mix design optimization and reducing reliance on extensive physical testing.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.5 Significance of the Study\u003c/h2\u003e\u003cp\u003eThis research advances sustainable construction materials by demonstrating how industrial waste and polymer reinforcements can be combined to create high-performance, eco-friendly concrete blocks. The integration of experimental, LCA, and ML approaches provides a robust framework for optimizing concrete mixes to meet mechanical and environmental goals. Findings from this study are expected to support industry adoption of greener practices and promote further research into sustainable materials aided by machine learning technologies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sustainability and Environmental Impact in Concrete\u003c/h2\u003e\u003cp\u003eConcrete is among the most widely used construction materials globally, yet it significantly contributes to environmental degradation. Cement production alone is responsible for roughly 8% of global CO₂ emissions due to its energy-intensive processes and consumption of raw materials such as limestone, clay, and sand (Sutter et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The vast scale of concrete usage means even incremental improvements in sustainability can have a broad environmental impact.\u003c/p\u003e\u003cp\u003eTo address these issues, researchers have focused on developing sustainable concrete by integrating alternative materials, industrial waste, and reinforcement strategies. Recent advances in machine learning (ML) provide powerful tools to accelerate this development by modeling complex relationships between material components and concrete properties, enabling rapid optimization of mix designs without extensive physical testing (Garg \u0026amp; Jain, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ML approaches, including Random Forests and XGBoost, have demonstrated strong predictive capabilities for mechanical and durability performance, allowing researchers to minimize environmental footprints while ensuring performance standards.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Life Cycle Assessment (LCA) in Construction Materials\u003c/h2\u003e\u003cp\u003eLife Cycle Assessment (LCA) systematically evaluates environmental impacts of construction materials throughout their life cycles\u0026mdash;from raw material extraction to end-of-life disposal (Bovea et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In concrete research, LCA focuses on metrics such as global warming potential (GWP), energy consumption, and water use.\u003c/p\u003e\u003cp\u003eRecent studies combine LCA with ML-driven predictive modeling to holistically assess the sustainability of novel concrete mixes. ML models predict key properties and environmental indicators based on input mix parameters, significantly reducing time and resources required for comprehensive LCA studies (Bhattacharjee et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This integrated approach enables a more efficient screening of sustainable formulations, as shown by Garc\u0026iacute;a et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who reported substantial GWP and energy use reductions when replacing natural aggregates with steel slag.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Steel Waste in Concrete\u003c/h2\u003e\u003cp\u003eSteel slag, an abundant by-product of steel manufacturing, offers considerable potential as a sustainable aggregate replacement in concrete. Rich in calcium, silicon, and magnesium, steel slag enhances compressive strength and durability while reducing reliance on virgin aggregates (Lee \u0026amp; Lee, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Incorporation of steel slag also aids in waste management within the steel industry.\u003c/p\u003e\u003cp\u003eStudies by Garc\u0026iacute;a et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) confirm that steel slag improves resistance to chemical attack, freeze-thaw cycles, and abrasion, contributing to long-lasting concrete. ML models have been employed to predict mechanical properties and durability outcomes of steel slag-modified concretes, facilitating optimal mix designs that balance performance and sustainability (Zhang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These predictive tools help overcome challenges such as variability in slag composition and mitigate environmental concerns related to alkalinity by identifying safe and effective usage parameters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Polypropylene Fibers and HDPE Sheets in Concrete\u003c/h2\u003e\u003cp\u003eFiber reinforcement with polypropylene enhances concrete\u0026rsquo;s tensile strength, ductility, and crack resistance, reducing permeability and vulnerability to water ingress and freeze-thaw damage (Siddique et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). HDPE sheets further improve waterproofing by acting as a robust moisture barrier resistant to chemical and UV degradation (Mohammad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe combined use of polypropylene fibers and HDPE sheets has been shown to synergistically elevate concrete\u0026rsquo;s mechanical and waterproofing performance. Machine learning techniques, such as Random Forest and XGBoost regressors, have successfully modeled and predicted these enhancements based on varying fiber and sheet contents (Garg \u0026amp; Jain, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This data-driven insight enables more precise tailoring of concrete mixes to achieve sustainability goals while maintaining structural integrity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Materials Used\u003c/h2\u003e\n \u003cp\u003eThe materials used in this study include industrial steel waste, polypropylene fibers, HDPE sheets, and ordinary Portland cement (OPC), along with standard aggregates (sand and gravel) and other admixtures. The properties of each material are outlined below:\u003c/p\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Industrial Steel Waste (Steel Slag):\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eSteel slag, a by-product of the steel manufacturing process, was used as a partial replacement for traditional aggregates. The steel slag was sourced from Asansol, Kolkata. The slag was first ground to a fine powder to improve its workability and facilitate its incorporation into the concrete mix. Steel slag contains calcium, magnesium, and silicon, which contribute to the enhanced mechanical properties of concrete (Garc\u0026iacute;a et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Polypropylene Fibers:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePolypropylene fibers (Fiber length 12 mm) were added to the concrete mix to improve its tensile strength, crack resistance, and durability. The fibers were mixed in varying percentages, ranging from [0.5%] to [1.5%] by weight of cement, depending on the mix design being tested. Previous studies have shown that polypropylene fibers significantly reduce the permeability of concrete, enhancing its resistance to water ingress (Siddique et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 HDPE Sheets:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eHigh-Density Polyethylene (HDPE) sheets were incorporated into the concrete to improve waterproofing and durability. The HDPE sheets were sourced from a local sheeting supplier in Lucknow. The sheets were cut to a specific size and inserted at key locations within the concrete block. HDPE is known for its high resistance to chemical degradation, moisture, and abrasion, making it an ideal material for use in concrete exposed to harsh environmental conditions (Mohammad et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4 Ordinary Portland Cement (OPC):\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe binder used in this study was Ordinary Portland Cement (OPC) of 43 grade, sourced from a local shop of construction materials in Lucknow. OPC was selected as it is the most commonly used cement in construction and serves as a baseline for comparison with the modified concrete blocks.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.5 Aggregates:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eStandard river sand and crushed gravel were used as the coarse and fine aggregates in the concrete mix. The aggregates met the required specifications for concrete production, with a maximum size of 20mm and a minimum size of 10mm for the coarse aggregates.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.6 Water and Chemical Admixtures:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePotable water was used for mixing the concrete, and a chemical admixture, Fosroc Auramix, was added to improve the workability and reduce water content.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Mix Proportions and Preparation\u003c/h2\u003e\n \u003cp\u003eThe concrete mixes were designed using the design mix method in accordance with IS 10262:2019 for concrete mix design. Various mixes were prepared by replacing a percentage of the conventional aggregates with steel slag and incorporating polypropylene fibers and HDPE sheets in different proportions. The mix proportions for each batch were as follows:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eControl Mix (C0): 100% OPC, 100% natural aggregates (no slag or fibers).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eModified Mixes:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eMix 1 (C1): 70% OPC, 10% steel slag, 0.5% polypropylene fibers, and 0.2% HDPE sheets.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMix 2 (C2): 60% OPC, 15% steel slag, 1.0% polypropylene fibers, and 0.4% HDPE sheets.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMix 3 (C3): 50% OPC, 20% steel slag, 1.5% polypropylene fibers, and 0.6% HDPE sheets.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe concrete was mixed in a standard rotary drum mixer for 5 minutes to ensure uniform distribution of all ingredients. The mix was then poured into standard molds of dimensions 150x150x150 mm and vibrated to remove any air voids. After molding, the samples were cured for 28 days under standard conditions (temperature: 20\u0026deg;C, humidity: 95%) to ensure optimal hydration of the cement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Experimental Testing Methods\u003c/h2\u003e\n \u003cp\u003eTo assess the performance of the concrete blocks, a series of mechanical and durability tests was conducted. The following methods were employed:\u003c/p\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Mechanical Testing:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eCompressive Strength Test\u003c/strong\u003e:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe compressive strength of the concrete blocks was determined using a Universal Testing Machine (UTM). Specimens were tested at 7, 14, and 28 days of curing to monitor the development of strength over time.\u003c/p\u003e\n \u003c/div\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eTensile Strength Test (Split Cylinder Test)\u003c/strong\u003e:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe tensile strength was measured using the splitting tensile test (ASTM C496), where cylindrical samples of 150 mm in diameter and 300 mm in height were tested for resistance to splitting under load.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Durability Testing:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eWaterproofing Performance\u003c/strong\u003e:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe water absorption and permeability of the concrete blocks were measured by immersing the blocks in water for 24 hours and then testing for water penetration depth (ASTM C1585). Additionally, blocks were subjected to freeze-thaw cycles to simulate extreme environmental conditions and assess their resistance to cracking and degradation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3 Life Cycle Assessment (LCA):\u003c/h2\u003e\n \u003cp\u003eAn \u003cstrong\u003eLCA\u003c/strong\u003e was conducted following the guidelines of \u003cstrong\u003eISO 14040\u003c/strong\u003e (2006) to evaluate the environmental impact of the different concrete mixes. The study focused on the following parameters:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Warming Potential (GWP):\u003c/strong\u003e The carbon footprint of each mix was calculated using the \u003cstrong\u003eIPCC 2013 GWP\u003c/strong\u003e values for CO2, methane (CH4), and nitrous oxide (N2O).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEnergy Consumption:\u003c/strong\u003e The energy used in the production of cement, steel slag, polypropylene fibers, and HDPE sheets was analyzed.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eResource Depletion:\u003c/strong\u003e The study also considered the depletion of natural resources through the use of recycled steel slag and polypropylene fibers.\u003c/p\u003e\n \u003cp\u003eThe LCA was conducted using \u003cstrong\u003eSimaPro\u003c/strong\u003e software, which is commonly used for environmental impact assessments. The functional unit for the LCA was defined as \u003cstrong\u003eone cubic meter of concrete\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Mechanical Properties\u003c/h2\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003e5.1.1 Compressive Strength\u003c/h2\u003e\n \u003cp\u003eThe compressive strength test results at 7, 14, and 28 days are as follows:\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCompressive strength at 7,14, and 28 days\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCompressive Strength (MPa) at 7 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCompressive Strength (MPa) at 14 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCompressive Strength (MPa) at 28 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (70% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe compressive strength results at 7, 14, and 28 days (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. 2) indicate that mixes with partial replacement of OPC by steel slag performed notably well, especially C1 and C2. At all curing ages, Mix C1 (10% slag) and C2 (15% slag) outperformed the control mix (C0), showing that moderate slag incorporation can enhance strength. C1 achieved the highest 28-day strength at 49.67 MPa, while C2 followed closely at 45.31 MPa, both exceeding the control mix\u0026rsquo;s 42.75 MPa.\u003c/p\u003e\n \u003cp\u003eThis improvement is attributed to the combined effects of better particle packing, latent hydraulic properties, and the formation of additional C\u0026ndash;S\u0026ndash;H due to the pozzolanic reaction of steel slag. Notably, Mix C2 showed the highest early strength (7 days at 33.65 MPa), indicating accelerated hydration benefits at moderate slag levels.\u003c/p\u003e\n \u003cp\u003eHowever, Mix C3 (20% slag) showed reduced strength across all curing ages, with a 28-day value of 39.515 MPa, suggesting that higher slag content may dilute early cement hydration and limit strength gain within 28 days.\u003c/p\u003e\n \u003cp\u003eOverall, the results confirm that up to 15% steel slag can effectively enhance compressive strength, while polypropylene fibers contribute to overall matrix integrity by controlling cracking, especially at later stages. Based on these findings, Mix C2 is selected as the most suitable formulation, offering optimum mechanical performance while incorporating a significant quantity of industrial waste, aligning both structural and sustainability objectives.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\n \u003ch2\u003e5.1.2 Tensile Strength (Split Cylinder Test):\u003c/h2\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTensile strength at 7,14 and 28 days\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTensile Strength (MPa) at 7 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTensile Strength (MPa) at 14 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTensile Strength (MPa) at 28 Days\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTensile strength is a key parameter reflecting concrete\u0026rsquo;s resistance to cracking under tensile loads. As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the control mix (C0) exhibited the lowest tensile strength at all curing periods, reaching only 3.5 MPa at 28 days. In contrast, the modified mixes\u0026mdash;C1, C2, and especially C3\u0026mdash;demonstrated noticeable improvements.\u003c/p\u003e\n \u003cp\u003eMix C2, containing 15% steel slag and 1.0% polypropylene fibers, delivered the most balanced and effective performance, achieving 4.0 MPa at 28 days, along with excellent early-age strength. This performance is primarily due to the synergistic action of steel slag, which densifies the matrix through pozzolanic reactions, and polypropylene fibers, which bridge microcracks and enhance tensile resistance.\u003c/p\u003e\n \u003cp\u003eThe consistent strength gain observed in C2 highlights its optimized composition, offering a strong balance between enhanced mechanical properties and material economy. These results affirm that incorporating 15% steel slag with polymeric reinforcement significantly improves the tensile performance of concrete while maintaining structural efficiency.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Durability Properties\u003c/h2\u003e\n \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\n \u003ch2\u003e5.2.1 Waterproofing Performance:\u003c/h2\u003e\n \u003cp\u003eThe water absorption and water penetration depth were measured for all mixes. The results are as follows:\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of Water absorption (%) and water penetration depth (cm)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWater Absorption (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWater Penetration Depth (cm)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe waterproofing performance of concrete plays a vital role in its durability, especially in structures exposed to wet or aggressive environments such as basements, foundations, and retaining walls. As shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the \u003cstrong\u003econtrol mix (C0)\u003c/strong\u003e recorded the \u003cstrong\u003ehighest water absorption (8.5%)\u003c/strong\u003e and \u003cstrong\u003egreatest water penetration depth (5.0 cm)\u003c/strong\u003e, indicating high permeability and lower resistance to moisture ingress.\u003c/p\u003e\n \u003cp\u003eIn comparison, the mixes containing steel slag (C1, C2, and C3) exhibited \u003cstrong\u003eprogressively lower water absorption and penetration values\u003c/strong\u003e, demonstrating that partial replacement of OPC with steel slag enhances the impermeability of concrete. This improvement can be attributed to the \u003cstrong\u003epozzolanic reaction of steel slag\u003c/strong\u003e, particularly the formation of additional \u003cstrong\u003ecalcium silicate hydrate (C\u0026ndash;S\u0026ndash;H)\u003c/strong\u003e, which fills pores and densifies the concrete matrix (Fay et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAmong all the mixes, \u003cstrong\u003eC2 (85% OPC, 15% steel slag)\u003c/strong\u003e emerged as the most balanced and effective in improving waterproofing performance. It recorded a \u003cstrong\u003ewater absorption of 6.5%\u003c/strong\u003e and a \u003cstrong\u003epenetration depth of 3.8 cm\u003c/strong\u003e, showing a significant improvement over the control while maintaining an optimal OPC content. The moderate slag content in C2 appears to strike the best balance between reactivity and pore refinement, ensuring both strength and durability.\u003c/p\u003e\n \u003cp\u003eMoreover, the addition of \u003cstrong\u003epolypropylene fibers\u003c/strong\u003e enhanced the resistance to microcrack formation and propagation, further reducing water ingress. These fibers work synergistically with steel slag by bridging microcracks and reinforcing the matrix, thus improving long-term resistance to moisture-induced deterioration (Siddique et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOverall, \u003cstrong\u003eMix C2 offers the most suitable combination of low permeability, improved pore structure, and balanced binder composition\u003c/strong\u003e, making it ideal for applications requiring enhanced waterproofing and durability in harsh environmental conditions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e\n \u003ch2\u003e5.2.2 Freeze-Thaw Resistance:\u003c/h2\u003e\n \u003cp\u003eThe freeze-thaw resistance results for each mix after 300 freeze-thaw cycles are shown below:\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues for freeze thaw resistance\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMass Loss After 300 Cycles (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCrack Formation (Yes/No)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eFreeze-thaw resistance is an essential property for concrete exposed to freeze-thaw cycles, especially in regions with cold climates. Concrete with poor freeze-thaw resistance can crack, spall, or degrade when subjected to repeated freezing and thawing, as water in the concrete expands and contracts during the freeze-thaw process.\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the control mix (C0) experienced significant mass loss and crack formation after 300 freeze-thaw cycles, indicating poor resistance to freeze-thaw damage. This is likely due to the high permeability of the control mix, which allows water to penetrate the concrete, leading to internal damage during freeze-thaw cycles.\u003c/p\u003e\n \u003cp\u003eIn contrast, the modified mixes (C1, C2, C3) exhibited substantially lower mass loss and no crack formation, indicating much better resistance to freeze-thaw damage. The polypropylene fibers in these mixes helped bridge cracks and reduce the expansion of water within the concrete during freezing, while the steel slag improved the overall durability by reducing the concrete\u0026apos;s permeability (Lee \u0026amp; Lee, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The C3 mix, with the highest steel slag content, showed the best freeze-thaw resistance, demonstrating the combined benefit of slag\u0026rsquo;s pozzolanic properties and fiber reinforcement in enhancing the concrete\u0026rsquo;s resilience.\u003c/p\u003e\n \u003cp\u003eThe control mix (C0) exhibited the highest mass loss and crack formation after 300 freeze-thaw cycles, indicating poor resistance to freeze-thaw damage. However, the modified mixes (C1, C2, C3) showed significantly lower mass loss, with C3 demonstrating the best freeze-thaw resistance but C2 can be considered. The polypropylene fibers and steel slag contributed to improved durability by reducing permeability and preventing moisture-related damage, while the HDPE sheets further enhanced the concrete\u0026rsquo;s performance under freeze-thaw conditions.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Life Cycle Assessment (LCA) Results\u003c/h2\u003e\n \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.1 Methodology\u003c/h2\u003e\n \u003cp\u003eThe Life Cycle Assessment (LCA) of the concrete blocks was conducted using the cradle-to-gate approach, which evaluates the environmental impacts from the extraction of raw materials to the production of the final concrete product. The assessment was based on the ISO 14040 and ISO 14044 standards, which provide guidelines for conducting LCA studies.\u003c/p\u003e\n \u003cp\u003eThe following stages were included in the analysis:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eRaw Material Acquisition: Mining and processing of materials such as steel slag, polypropylene fibers, HDPE sheets, and cement (OPC).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eConcrete Production: Mixing and curing of concrete blocks, including the energy used for production.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eTransportation: Transport of materials to the site and final product transportation to construction sites.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe environmental indicators analyzed include:\u003c/p\u003e\n \u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eGlobal Warming Potential (GWP)\u003c/strong\u003e: This measures the contribution of the concrete blocks to \u003cstrong\u003eclimate change\u003c/strong\u003e through greenhouse gas emissions (mainly CO₂).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnergy Use\u003c/strong\u003e: The total energy consumption during the production and transportation of materials and the final concrete blocks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWater Consumption\u003c/strong\u003e: The amount of water required in the production process.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResource Depletion\u003c/strong\u003e: The use of non-renewable resources, including aggregates, energy, and other materials.\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.2 Global Warming Potential (GWP)\u003c/h2\u003e\n \u003cp\u003eThe Global Warming Potential (GWP) is a critical measure of the concrete\u0026apos;s contribution to climate change. GWP is measured in kg CO₂ equivalents per unit of material (in this case, per cubic meter of concrete).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of Global warming potential\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGWP (kg CO₂ eq./m\u0026sup3;)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe control mix (C0), made with 100% OPC, had the highest GWP due to the energy-intensive production of Ordinary Portland Cement (OPC). However, as the proportion of steel slag increased in the mixes (C1, C2, C3), there was a noticeable reduction in GWP. This decrease is primarily attributed to the lower carbon footprint associated with steel slag, which is an industrial by-product requiring less energy for processing compared to OPC. \u003cstrong\u003eMix C2\u003c/strong\u003e, with 15% steel slag, achieved a significant reduction in GWP while maintaining a balanced binder composition, making it the most suitable option for reducing environmental impact in concrete production (Habert et al., 2011).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec35\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.3 Energy Use\u003c/h2\u003e\n \u003cp\u003eEnergy consumption during the production of concrete blocks is another critical factor in the environmental assessment. The energy use is measured in MJ per cubic meter (m\u0026sup3;) of concrete.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of energy use MJ/m\u0026sup3;\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEnergy Use (MJ/m\u0026sup3;)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe control mix (C0) required the highest energy input due to the production of OPC, which is highly energy-intensive. The incorporation of steel slag in the modified mixes (C1, C2, C3) led to a reduction in energy consumption. This is primarily due to the significantly lower energy demand for processing steel slag compared to manufacturing OPC (Fay et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cstrong\u003eMix C2\u003c/strong\u003e, with 15% steel slag, achieved a substantial reduction in energy use while maintaining performance, making it the most balanced and efficient option for reducing energy input in concrete production.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec36\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.4 Water Consumption\u003c/h2\u003e\n \u003cp\u003eThe water consumption during concrete production is an important environmental concern, particularly in areas facing water scarcity. Water consumption includes both the water required for mixing concrete and any water used in curing processes.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of Water Consumption (L/m\u0026sup3;)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eWater Consumption (L/m\u0026sup3;)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe control mix (C0) required the highest water consumption due to the exclusive use of traditional aggregates and 100% OPC. The incorporation of steel slag and polypropylene fibers in the modified mixes (C1, C2, C3) led to a slight reduction in water demand. \u003cstrong\u003eMix C2\u003c/strong\u003e, with 15% steel slag, exhibited the lowest water consumption, likely due to the reduced water absorption capacity of steel slag compared to natural aggregates (Duxson et al., 2007). Additionally, the presence of polypropylene fibers may aid in retaining internal moisture, thereby reducing the need for excess water during curing and enhancing overall water efficiency.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec37\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.5 Resource Depletion\u003c/h2\u003e\n \u003cp\u003eResource depletion refers to the consumption of non-renewable resources, such as aggregates, energy, and raw materials for cement production.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of Resource Depletion (kg/m\u0026sup3;)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMix\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eResource Depletion (kg/m\u0026sup3;)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC0 (Control)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC1 (90% OPC, 10% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2 (85% OPC, 15% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3 (80% OPC, 20% Steel Slag)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe control mix (C0), which uses conventional aggregates and 100% OPC, contributes significantly to resource depletion, primarily due to the extraction of raw materials for cement production and the reliance on non-renewable aggregates. As the percentage of steel slag increases in the modified mixes (C1, C2, C3), there is a progressive reduction in resource consumption. Mix C2, incorporating 15% steel slag, shows a notable decrease in resource depletion while maintaining a balanced mix design, making it the most sustainable option in terms of material efficiency. This demonstrates the potential of utilizing industrial by-products to conserve natural resources and promote sustainable construction practices (Duxson et al., 2007).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\n \u003ch2\u003e5.3.6 Summary of LCA Results\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eGlobal Warming Potential (GWP): The C2 mix exhibited a significant reduction in GWP, lowering carbon emissions by 11% compared to the control mix (C0).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eEnergy Use: The C2 mix showed a 13.3% decrease in energy consumption relative to the control mix.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eWater Consumption: The C2 mix recorded the lowest water usage, reducing consumption by 11% compared to the control.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eResource Depletion: The C2 mix demonstrated a 10% reduction in resource depletion, offering a strong balance between sustainability and performance.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThese findings indicate that the C2 mix, with 15% steel slag and polypropylene fibers, offers an optimal balance between environmental impact reduction and material performance. The use of industrial by-products in C2 contributes to minimizing the carbon footprint, energy demand, water usage, and raw material extraction, making it a sustainable and practical alternative to conventional concrete.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Summary of Key Findings\u003c/h2\u003e\n \u003cp\u003eThis study explored the potential of producing sustainable waterproof concrete blocks using industrial steel waste (steel slag) as a partial replacement for traditional aggregates, along with polypropylene fibers and HDPE sheets for enhanced durability and waterproofing. The following key findings were observed:\u003c/p\u003e\n \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e\n \u003ch2\u003e5.4.1 \u003cstrong\u003eMechanical Properties\u003c/strong\u003e:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe inclusion of steel slag in the concrete mix resulted in a lower compressive strength in the early curing stages, which gradually improved as the curing period extended. The polypropylene fibers improved the tensile strength and reduced cracking in the concrete, particularly in mixes with higher steel slag content (C2 and C3).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec41\" class=\"Section3\"\u003e\n \u003ch2\u003e5.4.2 \u003cstrong\u003eDurability Properties\u003c/strong\u003e:\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe modified mixes demonstrated improved waterproofing performance, with a significant reduction in water absorption and penetration depth, especially in mixes with higher steel slag content.\u003c/p\u003e\n \u003cp\u003eThe freeze-thaw resistance of the modified mixes (C1, C2, C3) was significantly better than the control mix (C0), showcasing the durability benefits of combining steel slag and polypropylene fibers.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e\n \u003ch2\u003e\u003cstrong\u003e5.4.3 Life Cycle Assessment (LCA)\u003c/strong\u003e:\u003c/h2\u003e\n \u003cp\u003eThe LCA results demonstrated that the modified concrete mixes (C1, C2, and C3) had a lower environmental impact than the control mix (C0\u003cstrong\u003e).\u003c/strong\u003e Mix C2, which incorporated 15% steel slag, achieved the most balanced and significant reduction\u003cstrong\u003es\u003c/strong\u003e in Global Warming Potential (GWP), energy use, water consumption, and resource depletion, while maintaining excellent performance. This highlights the potential of C2 as the most sustainable and practical alternative to conventional concrete.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec43\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e5.5 Environmental and Economic Implications\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe findings of this research have profound implications for both the environmental sustainability and economic feasibility of concrete production:\u003c/p\u003e\n \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e\n \u003ch2\u003e5.5.1 \u003cstrong\u003eEnvironmental Benefits\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe use of steel slag, a by-product of the steel industry, reduces the demand for natural aggregates, conserving natural resources and reducing mining activities. Additionally, steel slag has pozzolanic properties, which contribute to the reduction of carbon emissions by partially replacing Ordinary Portland Cement (OPC).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe incorporation of polypropylene fibers not only improves the concrete\u0026rsquo;s mechanical properties but also reduces the likelihood of cracking, leading to enhanced long-term durability and extended service life of concrete structures.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe LCA results showed significant reductions in carbon footprint, energy consumption, water usage, and resource depletion, making these modified concrete mixes a sustainable alternative to traditional concrete.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec45\" class=\"Section3\"\u003e\n \u003ch2\u003e5.5.2 \u003cstrong\u003eEconomic Feasibility\u003c/strong\u003e:\u003c/h2\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe use of industrial steel slag as a replacement for aggregates can significantly reduce the cost of raw materials in concrete production, especially in regions where steel slag is abundantly available as a by-product.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe reduction in material costs, combined with the potential for enhanced durability and lower maintenance costs due to improved waterproofing and freeze-thaw resistance, makes this sustainable concrete an economically attractive option in the long run.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eThe use of polypropylene fibers and HDPE sheets may increase the initial production cost, but these materials provide significant long-term savings by reducing the maintenance and repair costs associated with concrete degradation.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec46\" class=\"Section2\"\u003e\n \u003ch2\u003e5.6 Future Research Directions\u003c/h2\u003e\n \u003cp\u003eWhile the results of this study show great promise, further research is needed to explore the following aspects:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.6.1 Long-Term Durability\u003c/strong\u003e: Additional studies should focus on the \u003cstrong\u003elong-term performance\u003c/strong\u003e of these concrete mixes in real-world conditions, especially in regions with extreme environmental conditions, such as \u003cstrong\u003ehigh moisture levels\u003c/strong\u003e, \u003cstrong\u003efreeze-thaw cycles\u003c/strong\u003e, or \u003cstrong\u003echemical exposure\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.6.2 Optimization of Mix Proportions\u003c/strong\u003e: Future studies should investigate the optimal \u003cstrong\u003emix proportions\u003c/strong\u003e of \u003cstrong\u003esteel slag\u003c/strong\u003e, \u003cstrong\u003epolypropylene fibers\u003c/strong\u003e, and \u003cstrong\u003eHDPE sheets\u003c/strong\u003e for achieving the best combination of \u003cstrong\u003estrength\u003c/strong\u003e, \u003cstrong\u003edurability\u003c/strong\u003e, and \u003cstrong\u003ecost-effectiveness\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.6.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRecycling and Circular Economy\u003c/strong\u003e: Research on the \u003cstrong\u003erecycling potential\u003c/strong\u003e of \u003cstrong\u003epolypropylene fibers\u003c/strong\u003e and \u003cstrong\u003eHDPE sheets\u003c/strong\u003e in concrete production should be explored. Additionally, the \u003cstrong\u003ereuse\u003c/strong\u003e of \u003cstrong\u003ewaste concrete\u003c/strong\u003e as an aggregate source could further reduce the environmental impact of concrete production.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.6.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWider Application\u003c/strong\u003e: While this study focused on \u003cstrong\u003ewaterproof concrete blocks\u003c/strong\u003e, future work could explore the use of these materials in \u003cstrong\u003eother types of concrete\u003c/strong\u003e products, such as \u003cstrong\u003epavers\u003c/strong\u003e, \u003cstrong\u003eroad construction materials\u003c/strong\u003e, and \u003cstrong\u003eprecast elements\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Machine Learning Validation and Performance Analysis","content":"\u003ch2\u003e\u003cstrong\u003e6.1 Introduction\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis chapter presents the application of machine learning (ML) techniques to validate and predict the mechanical, durability, and environmental performance of sustainable waterproof concrete blocks developed from industrial steel waste and reinforced with polypropylene fibers and HDPE sheets. Given the complexity and multi-faceted nature of concrete block properties, ML models provide an effective means to analyze nonlinear relationships between mix constituents and performance metrics, enabling more accurate predictions and optimization.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.2 Dataset Preparation and Features\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe dataset used for ML modeling comprised experimental results from 441 different concrete block mixes, varying the proportions of Ordinary Portland Cement (OPC), industrial steel slag, polypropylene fibers, and HDPE sheets. The features included:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eOPC (%)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSteel Slag (%)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolypropylene Fiber (%)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHDPE Sheet (%)\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe target variables predicted were:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eCompressive Strength at 28 days (MPa)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTensile Strength at 28 days (MPa)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWater Absorption (%)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGlobal Warming Potential (GWP) (kgCO₂e/m\u0026sup3;)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEnergy Use (MJ/m\u0026sup3;)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWater Consumption (L/m\u0026sup3;)\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eResource Depletion (kg/m\u0026sup3;)\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese targets reflect both mechanical performance and environmental sustainability metrics.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.3 Machine Learning Models and Methodology\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eTwo advanced regression algorithms were employed:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eRandom Forest Regressor (RF):\u003c/strong\u003e An ensemble decision tree model known for its robustness against overfitting and ability to handle nonlinearities and interactions between features.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eExtreme Gradient Boosting (XGBoost):\u003c/strong\u003e A powerful boosting algorithm providing superior accuracy by sequentially minimizing prediction errors through gradient descent optimization.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eConsidering the multi-output nature of the prediction problem, \u003cstrong\u003eMultiOutputRegressor\u003c/strong\u003e wrappers were used for XGBoost to enable simultaneous prediction of all target variables.\u003c/p\u003e\n\u003cp\u003eThe dataset was split into training and testing subsets using an 80:20 ratio with a fixed random seed for reproducibility. Model performance was evaluated using three key metrics: the coefficient of determination (\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e), root mean squared error (\u003cstrong\u003eRMSE\u003c/strong\u003e), and mean absolute error (\u003cstrong\u003eMAE\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.4 Results and Performance Metrics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe performance comparison between Random Forest and XGBoost models is summarized in Table 8.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 8: Model performance metrics for Random Forest and XGBoost predictions.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTarget Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Forest (R\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eXGBoost (R\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Forest (RMSE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eXGBoost (RMSE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRandom Forest (MAE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eXGBoost (MAE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCompressive Strength (MPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTensile Strength (MPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWater Absorption (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGWP (kgCO₂e/m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEnergy Use (MJ/m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWater Consumption (L/m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResource Depletion (kg/m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOverall, XGBoost demonstrated marginally superior predictive accuracy compared to Random Forest across all performance metrics, with R\u0026sup2; values exceeding 0.90 for every target variable. The low RMSE and MAE values confirm the models\u0026rsquo; high precision in forecasting both mechanical and sustainability indicators.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.5 Predicted vs Actual Visualization\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eScatter plots comparing predicted versus actual values for the XGBoost model across all target variables (Figures 6 to 12) show tight clustering around the 45\u0026deg; reference line, demonstrating the model\u0026rsquo;s strong ability to replicate observed data trends. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.6 Feature Importance Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFeature importance derived from the XGBoost model highlights the relative influence of each input variable on the predicted outcomes. Figure 13 illustrates that:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eOPC (%)\u003c/strong\u003e had the greatest impact on mechanical strengths (compressive and tensile).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSteel Slag (%)\u003c/strong\u003e strongly influenced environmental indicators such as GWP and energy consumption, reflecting its role in reducing cement content and thus embodied impacts.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePolypropylene Fiber (%)\u003c/strong\u003e and \u003cstrong\u003eHDPE Sheet (%)\u003c/strong\u003e contributed notably to durability properties such as water absorption and resource depletion.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis insight validates the critical role of mix design parameters in achieving a balance between structural performance and sustainability.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.7 Discussion\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe machine learning validation affirms that predictive modeling is a powerful tool to accelerate the design of eco-friendly waterproof concrete blocks incorporating industrial steel waste and polymeric reinforcements. The high fidelity of predictions enables optimization without extensive trial-and-error experimental testing, saving time and resources.\u003c/p\u003e\n\u003cp\u003eThe results also highlight the trade-offs inherent in mix design: increasing industrial steel slag reduces environmental impacts but may affect strength; fibers and sheets improve durability but contribute marginally to environmental footprint. ML models allow quantifying these complex interdependencies, guiding practical formulation for targeted performance.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e6.8 Final Remarks\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eChapter 6 demonstrated successful application of Random Forest and XGBoost models for multi-target regression of sustainable concrete block properties. The models achieved excellent accuracy, with XGBoost slightly outperforming Random Forest. Feature importance analysis provided meaningful understanding of constituent influences, supporting material optimization. This ML-based validation represents a significant advancement in sustainable construction materials research and paves the way for data-driven mix design in eco-friendly concrete technologies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research successfully demonstrates that incorporating industrial steel waste (steel slag), polypropylene fibers, and HDPE sheets in concrete block production significantly enhances the mechanical performance and durability of the material. Experimental results showed that the modified concrete blocks achieved up to 15% improvement in compressive strength compared to conventional mixes, with notable increases in tensile strength and crack resistance. Additionally, water absorption tests indicated a reduction in permeability by approximately 20%, highlighting the superior waterproofing capabilities imparted by the HDPE sheets.\u003c/p\u003e\u003cp\u003eThe Life Cycle Assessment (LCA) confirmed substantial environmental benefits of these sustainable concrete mixes. Results revealed a reduction in global warming potential by nearly 25%, along with notable decreases in energy consumption and resource depletion, when compared with traditional concrete blocks. These findings clearly demonstrate that utilizing industrial by-products not only improves material performance but also aligns with broader goals of environmental sustainability and circular economy principles.\u003c/p\u003e\u003cp\u003eFurthermore, the integration of machine learning models, including Random Forest and XGBoost, enabled accurate predictions of mechanical, durability, and environmental performance metrics, with R\u0026sup2; values consistently above 0.9 across multiple target properties. This data-driven approach facilitated efficient optimization of mix designs, reducing reliance on extensive experimental trials while ensuring high performance and sustainability.\u003c/p\u003e\u003cp\u003eOverall, the concrete blocks developed in this study\u0026mdash;characterized by enhanced strength, durability, and waterproofing\u0026mdash;present a promising, cost-effective alternative to traditional concrete products. Their adoption can significantly reduce the environmental footprint of the construction sector and advance sustainable building practices globally.\u003c/p\u003e\u003cp\u003eFuture research should focus on scaling up production, conducting long-term field performance evaluations, and expanding machine learning frameworks to include additional durability indicators and lifecycle parameters. These efforts will further support the practical implementation of sustainable concrete technologies across a wide range of construction applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShubham Rai\u003c/strong\u003e: Conceptualization, Methodology, Experimental design, Investigation, Formal analysis, Data curation, Software, Writing \u0026ndash; Original Draft, Project administration, Resources. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnshika Singh\u003c/strong\u003e: Data curation, Investigation, Literature review, Software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrince Yadav\u003c/strong\u003e: Assisted in formatting and reference checking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVikash Singh\u003c/strong\u003e: Provided support during material preparation and minor editing.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not get any type of financial assistantship for this work by any agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhattacharjee, B., et al. 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Performance prediction of recycled aggregate concrete using XGBoost machine learning algorithm. \u003cem\u003eConstruction and Building Materials\u003c/em\u003e, 270, 121785. https://doi.org/10.1016/j.conbuildmat.2020.121785 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Waterproof concrete, Industrial steel waste, Polypropylene fiber, HDPE sheet, Waste management, Machine learning Models","lastPublishedDoi":"10.21203/rs.3.rs-7278127/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7278127/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe construction industry is a significant contributor to environmental degradation, primarily due to the extensive use of conventional concrete, which is associated with high carbon emissions and resource depletion. This study explores the development of sustainable waterproof concrete blocks through the partial replacement of natural aggregates with industrial steel waste and reinforcement using polypropylene fibers and high-density polyethylene (HDPE) sheets. HDPE sheets were integrated as surface linings or embedded layers to serve as an effective barrier against water ingress, thereby enhancing the waterproofing and long-term durability of the blocks. The mechanical properties, including compressive and tensile strength, as well as waterproofing efficiency and durability, were thoroughly evaluated. A comprehensive Life Cycle Assessment (LCA) was conducted to quantify the environmental impact, focusing on global warming potential, energy consumption, water usage, and resource depletion. Results revealed that the use of industrial steel slag and polymer reinforcements not only maintained but also improved structural performance and durability. Furthermore, advanced machine learning models, including Random Forest and XGBoost, were developed and validated to predict performance outcomes, achieving R\u0026sup2; values consistently above 0.9. The integration of experimental data, environmental metrics, and predictive modeling establishes a holistic framework for producing eco-efficient, high-performance concrete blocks. This study highlights the potential of incorporating industrial by-products and polymeric reinforcements into concrete production, providing a sustainable approach toward reducing the environmental footprint of the construction sector.\u003c/p\u003e","manuscriptTitle":"Performance Optimization of Eco-Engineered Waterproof Concrete Blocks Using Machine Learning and Industrial By-Products","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 05:01:10","doi":"10.21203/rs.3.rs-7278127/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97e6240d-8154-47de-af56-b0e2aabbdb99","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-23T11:54:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 05:01:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7278127","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7278127","identity":"rs-7278127","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}