Investigation on Permeability Behavior of Open and Dense Graded Asphalt Mixes and Their Effect on Surface Runoff

Investigation on Permeability Behavior of Open and Dense Graded Asphalt Mixes and Their Effect on Surface Runoff | 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 Investigation on Permeability Behavior of Open and Dense Graded Asphalt Mixes and Their Effect on Surface Runoff Yateen Lokesh, Harshad R Parate, Rajashekhar Swamy H M, Shreyas T This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4287453/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Effectively managing stormwater runoff is crucial for minimizing the adverse impact of heavy rainfall events such as flooding and irrecoverable damages in asphalt pavements. To optimize stormwater management strategies and prevent flooding of roads and streets, it is essential to understand the structural, functional and durability behavior of both dense and open graded bituminous pavements under adverse rainfall conditions. The present study aimed to investigate a trial/Selected Grade (SG), Open Graded Friction Course (OGFC) as per IRC: 129-2019 and Bituminous Concrete Grade-2 (BC-2) with VG-40, PMB-40, and CRMB-55 as binders. Additionally, pelletized cellulose fibers (0.3% by weight of aggregates) and induction sensitive steel fibers (5% by weight of aggregates) were used only in SG and OGFC mixes for the study. The Marshall specimens prepared for the mentioned mixes were subjected to various laboratory tests to investigate and compare the structural, functional and durability characteristics. The laboratory results obtained were later used to compare, simulate and observe the behavior of the mentioned mixes to manage surface runoff and curb urban flooding using SCS Curve Number Method and QSWAT3-1.6.5 software. Koramangala in Bangalore city was selected as the potentially flood prone zone among 10 severely vulnerable zones and 19 moderately vulnerable zones for simulation based on Bruhat Bengaluru Mahanagara Palike’s (BBMP’s) urban flood report for the year 2020-21. The research findings indicates that BC-2 possessed 25.11% better Marshall Stability and 2.65% better Indirect Tensile Strength (ITS) compared to the open graded mixes (OGFC and SG) with CRMB-55 as binder. It was also observed that BC-2 had 7.11% more resistance to moisture compared to open graded mixes (OGFC and SG) with CRMB-55 as binder. On the contrary, among the open graded mixes, OGFC indicated 91.66% and 81.26% better permeability and abrasion resistance respectively compared to BC-2 mix. Based on the rainfall simulation conducted it was observed that the surface runoff on BC-2 mix using SCS Curve Number Method was 42.18% and using QSWAT3-1.6.5 software was 31.25% more than the open graded mixes considered for the study. The simulation also indicated that BC-2 mix had 30.07% more discharge than the open graded mixes. It was also observed that open graded mixes had 65.70% more infiltration compared to BC-2 mix. The research concludes that the open graded mixes are comparatively less strong than BC-2 mix but, on the contrary performs better in curbing urban floods and hence could be considered as a sustainable infrastructure solution. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. INTRODUCTION Conventional bituminous pavements are provided with a wearing course which is expected to be impervious and cater better riding comfort to the motor vehicle users. These wearing course are usually constructed using considerably superior quality of materials compared to the underlying layers. Due to rapid urbanization, the available natural land are usually altered and utilized to develop the infrastructure to meet the growing needs. Majority of the land gets covered by structures and paved for better transportation network and connectivity. This lately has deprived the potential of the natural surface to infiltrate the runoff water during monsoon seasons and to recharge the ground water. Such an anthropogenic change has led to sever floods in many areas and sever draught in other. Urban areas are usually the most effected regions especially during monsoon seasons where the streets are seen to be flooded disrupting the daily chores of the public dwelling in the region dependent on road networks for commutes. These urban floods are usually due to several factors like improper town planning, improper hydrological impact studies, unauthorized land acquisitions, construction floss, poorly planned, designed and connected stormwater drain system and lack of maintenance of these infrastructural facilities. Bituminous pavements being sensitive to moisture are forcibly exposed to such floods for a prolonged duration which greatly impacts their service life due to structural and functional distresses. Several road user deaths are reported in many cities due to urban floods and pavement failure during monsoon season. Similarly, along roads networks in such regions, with slightly high vehicle operating speeds, vehicles face poor visibility due to water splash and has led to sever life threatening accidents. Rainfall is a vital component of the Earth's water cycle, but excessive or poorly managed rainfall can have detrimental effects on infrastructure, particularly roads. The need for using advanced technology has significantly increased in order to mitigate these impacts. Utilization and implementation of Porous Asphalt Concrete (PAC) along urban road networks could be one such management strategies which could be adopted to curb the detrimental effects of urban flooding. But it has its own limitations to be used on roads for motor vehicles to ply upon in terms of strength related capabilities and hence are used usually on vehicular parking areas. Several researches are still being conducted worldwide to make this technology viable and sustainable to be used in place of conventional wearing course bituminous mixes. Permeability refers to the ability of a material to allow the passage or flow of fluids, such as liquids or gases, through its structure. In the context of PAC, permeability is a crucial property that plays a significant role in how this specialized pavement layer functions. Permeability is a fundamental property of PAC that allows it to effectively manage rainwater and its associated challenges. By enabling stormwater to infiltrate the road surface, porous asphalt not only helps mitigate flooding by channelizing the water along the designated path and medium, prevent erosion, and curb pollution but also promotes safer driving conditions and more sustainable urban environment. Several researchers have conducted studies on this area about the permeability characteristics of PAC. (R. Choudhary et al, 2020 , J. Chen et al, 2017 , H. Xu et al,2016, M.N. Akhtar et al,2021). The results clearly demonstrate that both field and laboratory tests were conducted, and laboratory tests, in particular, offer a controlled environment facilitating careful monitoring and control of variables. In the laboratory permeability tests, loose mixtures were subjected to compaction using varying blows from a Marshall compactor. Notably, the compacted samples exhibited lower permeability compared to cores, attributable to the greater thickness of the compacted specimens. Specifically, the thickness of the compacted specimens ranged from 60 to 70 mm, while the cores of the samples varied between 20 to 40 mm in thickness. The pavement, as assessed through the laboratory tests, displayed an average permeability ranging from 4.16 cm/s to 5.18 cm/s using the NCAT method and from 0.66 cm/s to 0.89 cm/s using the ASTM method. The literature on analysis was studied on watersheds (Krishnan, G, 2018), which focused on the critical importance of managing water resources, especially in the context of increasing water demands, declining water supplies, and climate change. The article also mentions the use of SWAT in various regions of India for estimating runoff and surface yield. The specific study area, the Nethravathi river basin in Karnataka, India, is introduced. The article provides monthly model calibration and validation results, showcasing the performance of the SWAT model in simulating peak runoff. During calibration, the model simulated a maximum peak runoff of 1800 m³/s compared to the observed value of 1650 m³/s in August 1997. In the validation phase, the simulated peak runoff was 1600 m³/s, while the observed value was 1390 m³/s in August 2005. It reports an average annual rainfall of 3721 mm in the basin, with an estimated average annual runoff of 1130 mm, equivalent to approximately 30% of the average annual rainfall. (Madhusudan et al,2021), The approach involves dividing the watershed into 11 sub-watersheds and further subdividing them into 29 HRUs based on land use/land cover (LU/LC) maps, soil maps, and slope variations. The article provides information on the time periods for calibration and validation, with calibration performed from 2011 to 2015 and validation from 2016 to 2020. The article reports an initial R² value of 0.396 before calibration, indicating that the model over predicted the Shimsha River's flow capability. (Mohammad, Ansari, A, 2016 ). K, Shanbor and J, Manoj. (2017). The article outlines the objective of the current research, which is to estimate the runoff volume and sediment load of Duhok Dam Reservoir from the beginning of its operation in 1988 to 2011 using the SWAT model. It reports an average annual runoff of 14.3 million cubic meters (MCM) and an average annual sediment load of 124.6*10^3 tons for the period from 1988 to 2011. These results offer valuable insights into the hydrology of the Duhok Dam watershed over this time span. (J. Chen et al ,2017, H. Xu et al ,2016, Y. Yan et al. ( 2016 ), K, Jan et al., ( 2017 ). The research results indicate that with an escalation in rainfall intensity, both surface runoff and permeability coefficient of the Open-Graded Friction Course (OGFC) mixture, characterized by 24.8% air voids, demonstrate an increase. The study findings suggest that the occurrence of freeze-thaw cycles can result in heightened water infiltration and erosion within the asphalt mixture, with a pronounced impact on open-graded mixtures. 2. OBJECTIVES The objectives of the present study was to: Investigate and compare the structural, functional and durability characteristics of SG, OGFC and BC-2 with VG-40, PMB-40 and CRMB-55 as binders in Marshall specimens, having constant pelletized cellulose fibers (0.3% by weight of aggregates) and induction sensitive steel fibers (5% by weight of aggregates) in all the mix categories during the study Conduct flood simulation analysis, compare the behavior of SG, OGFC and BC-2 mixes as wearing course in mitigating floods at Koramangala valley of Bangalore city, India to determine surface runoff on pavements with SG, OGFC and BC-2 as wearing course using Curve Numbers (CN) method and QSWAT Software (with different CNs based on soil characteristics, which, in turn, depend on the coefficient of permeability) 3. METHODOLOGY The study methodology comprised three main steps: Preparation of Mixes : Marshall Specimens for Selected Grade (SG), Open Graded Friction Course (OGFC), and Bituminous Concrete (BC-2) were prepared. These mixes utilized different binders (VG-40, PMB-40, and CRMB-55) while maintaining a consistent proportion of pelletized cellulose fibers (0.3% by weight of aggregates) and induction-sensitive steel fibers (5% by weight of aggregates). Laboratory Evaluation : Structural, functional, and durability characteristics of the mixes were assessed at the Optimum Binder Content (OBC). This involved conducting a thorough comparison to analyze their performance. Flood Simulation Analysis : Flood simulation was carried out using the Curve Number (CN) method and QSWAT software. Various curve numbers were applied based on the permeability attributes of the mixes under consideration. 4. RESULT ANALYSIS Marshall Specimens were prepared for Selected Grade (SG), Open Graded Friction Course (OGFC), and Bituminous Concrete (BC-2) using VG-40, PMB-40, and CRMB-55 binders. These specimens contained consistent proportions of pelletized cellulose fibers (0.3% by weight of aggregates) and induction-sensitive steel fibers (5% by weight of aggregates). The aim was to determine the Optimum Binder Contents (OBCs) and compare the Marshall properties of each mix. The results of these evaluations are presented in Table 1 , Table 2 , and Table 3 , respectively. Table 1 Marshall Properties (Average) of SG, OGFC and BC-2 at OBC with VG-40 Gradation Mixes OGFC (50 Blows) Selected Grade (SG) (50 Blows) BC-2 (75 Blows) Parameters Optimum Binder Content, OBC (%) 5.70 5.70 5.66 Bulk Density of specimen, Gb (gm/cc) 2.122 2.190 2.56 Theoretical Density, Gt (gm/cc) 2.73 2.725 2.704 Volume of air voids in mix, Vv, % 22.23 19.65 5.44 Total Volume of Bitumen Binder, Vb, % 11.97 12.36 14.33 Voids in Mineral Aggregates, VMA, % 34.20 32.01 19.77 Voids Filled with Bitumen, VFB, % 35.02 38.76 72.79 Corrected Stability, Kg 785.53 759.01 1060.86 Flow, mm 4.40 5.27 5.20 Table 2 Marshall Properties (Average) of SG, OGFC and BC-2 at OBC with PMB-40 Gradation Mixes OGFC (50 Blows) Selected Grade (SG) (50 Blows) BC-2 (75 Blows) Parameters Optimum Binder Content, OBC (%) 6 6 5.5 Bulk Density of specimen, Gb (gm/cc) 2.16 2.20 2.64 Theoretical Density, Gt (gm/cc) 2.71 2.71 2.71 Volume of air voids in mix, Vv, % 20.28 18.74 2.50 Total Volume of Bitumen Binder, Vb, % 12.83 13.10 14.39 Voids in Mineral Aggregates, VMA, % 33.11 31.84 16.89 Voids Filled with Bitumen, VFB, % 38.77 41.65 85.93 Corrected Stability, Kg 1244.45 1230.92 1675.39 Flow, mm 5.20 7.7 6.73 Table 3 Marshall Properties (Average) of SG, OGFC and BC-2 at OBC with CRMB-55 Gradation Mixes OGFC (50 Blows) Selected Grade (SG) (50 Blows) BC-2 (75 Blows) Parameters Optimum Binder Content, OBC (%) 6 6 6 Bulk Density of specimen, Gb (gm/cc) 2.183 2.22 2.63 Theoretical Density, Gt (gm/cc) 2.71 2.71 2.69 Volume of air voids in mix, Vv, % 19.61 17.82 2.00 Total Volume of Bitumen Binder, Vb, % 12.97 13.24 15.66 Voids in Mineral Aggregates, VMA, % 32.58 31.07 17.66 Voids Filled with Bitumen, VFB, % 39.81 42.63 88.87 Corrected Stability, Kg 1369.61 1359.20 1828.89 Flow, mm 5.39 7.9 6.88 4.1 ITS and TSR: The specimens prepared at the Optimum Binder Content (OBC) for the various mixes underwent laboratory testing, specifically the Indirect Tensile Strength Test (ITS) and Tensile Strength Ratio (TSR), in accordance with AASHTO-T-283 specifications. This allowed for the examination and comparison of their tensile strength characteristics and moisture sensitivity. The results of these tests are detailed in Table 4 and Table 5 , while their graphical representations can be found in Fig. 1 and Fig. 2 , respectively. Table 4 Average Indirect Tensile Strength (ITS) test results (KPa) Binder Type OGFC SG BC-2 VG 40 Unconditioned 730.31 510.52 520.12 Conditioned 570.91 385.26 484.56 PMB 40 Unconditioned 662.76 527.18 568.34 Conditioned 578.02 448.50 526.58 CRMB 55 Unconditioned 877.63 833.88 837.40 Conditioned 787.77 717.59 809.29 Table 5 Average Tensile Strength Ratio (TSR) test results (%) Binder OGFC SG BC-2 VG 40 78.17 75.42 85.07 PMB 40 87.21 85.07 92.65 CRMB 55 89.76 86.05 96.64 The findings reveal that in the case of Selected Grade (SG) with VG 40 as the binder, the Tensile Strength Ratio (TSR) stood at 75.42%, while for Open Graded Friction Course (OGFC), it reached 78.17%, surpassing SG's TSR. Conversely, when PMB 40 was the binder, SG demonstrated a superior TSR of 85.07% compared to OGFC's 87.21%. However, with CRMB 55 as the binder, OGFC showcased a higher TSR of 89.76% compared to SG's TSR of 86.05%. 4.2 Drain Down Test: In Table 6 & Fig. 3 , the Drain Down test reveals a drain down of 2.32% for Open Graded Friction Course (OGFC) and 3.25% for Selected Grade (SG) when VG 40 is used as the binder. The results indicate a 1.8% increase when CRMB 55 is compared to VG 40. According to IRC 129: 2019 standards, the maximum allowable value for Drain Down is 0.3. In this study, the values for OGFC exceed this limit, remaining at 2.32%, while SG, the selected grade, records 3.25%. However, it's noteworthy that SG's result is comparatively better than OGFC's and still complies with Indian Standards. Table 6: Average Drain-Down Test results (%) Binder OGFC SG BC-2 VG 40 2.32 3.25 5.64 PMB 40 3.91 6.54 8.74 CRMB 55 4.48 7.38 9.37 4.3 Cantabro Abrasion Test: In Table 7 & Fig. 4 , it is evident that the selected grade experiences a lower abrasion loss, at 6.45%, compared to the OGFC, which exhibits a higher abrasion loss of 7.25%. This comparison indicates that the selected grade outperforms the OGFC in terms of gradation. Table 7 Average Cantabro Abrasion Test results (%) Binder OGFC SG BC-2 VG 40 7.25 6.45 2.40 PMB 40 10.97 10.08 2.52 CRMB 55 12.60 12.20 2.36 4.4 Permeability Test: In Table 8 & Fig. 5 , it is evident that the permeability of VG 40 OGFC is 21% higher than that of VG 40 SG. Similarly, with PMB 40 and CRMB 55, the permeability of OGFC is 10% higher compared to SG. Furthermore, when compared to VG 40 BC-2, VG 40 OGFC exhibits a significantly higher permeability, with a staggering 91% difference. Table 8 Results of Permeability Test (cm/s) with Voids (%) Binder % Voids OGFC cm/s % Voids SG cm/s % Voids BC-2 cm/s VG 40 22.23 0.0108 20.52 0.0084 4.83 0.00093 PMB 40 20.27 0.0085 19.97 0.0077 CRMB 55 19.61 0.0078 17.82 0.0071 4.5 Simulation for Flood Analysis using QSWAT Software: The analysis involved the estimation of surface runoff and discharge utilizing QSWAT Software and the SCS Curve Number method. For this study, Curve Numbers (CN) of 76, 82, and 98 were utilized. CN 76 and CN 82 were representative of pervious pavement, while CN 98 corresponded to BC-2 as represented in Fig. 6 –8 respectively. In accordance with the roster of susceptible locations recognized by the Bruhat Bengaluru Mahanagara Palike (BBMP) for the 2020-21 period, the study zone selected was the K-Valley (Koramangala Valley). The composition of land usage within the research site, comprising 73.07% residential areas and 26.93% pavements, was derived from QGIS, encompassing a total area of 253.58 hectares. The study encompassed estimations of surface runoff, discharge, and infiltration 4.6 Surface Runoff: Table 9 & Fig. 9 illustrates rainfall trends over a period of five years, demonstrating fluctuations in runoff across various Curve Numbers (CN). It is noteworthy that CN 76 shows reduced runoff, whereas CN 98 indicates higher runoff levels for similar rainfall intensities. This study emphasizes noticeable differences between CN 76 and CN 98. Notably, the shift from pervious surfaces represented by CN 76 to impervious pavement represented by CN 98 led to an increase in surface runoff from 4230.35 mm in 2018 to 6417.75 mm in 2022. Table 9 Rainfall (mm) v/s Surface Runoff (mm) from Year 2018 to 2022 CN 76 CN 82 CN 98 Year Rainfall, mm Surface Runoff, mm 2018 1686.04 796.23 821.86 1204.69 2019 1872.68 915.95 941.11 1332.33 2020 1688.86 775.43 803.95 1198.98 2021 1871.06 933.03 963.11 1373.42 2022 1734.01 737.32 757.26 1216.54 Total 8840.56 4230.36 4287.28 6417.75 4.7 Discharge and Infiltration: Table 10 & Fig. 10 presents the relationship between rainfall and discharge (measured in m³/s) spanning five years, showcasing fluctuations in discharge levels among various Curve Numbers (CN). Table 11 & Fig. 11 illustrates the infiltration process, revealing a decline in infiltration rates as CN values increase in reaction to rainfall. Notably, the shift from a pervious pavement with CN 76 to an impervious pavement with CN 98 led to a decrease in infiltration from 278.04 mm in 2018 to 95.34 mm in 2022. Table 10 Rainfall (mm) v/s Discharge (m³/s) from Year 2018 to 2022 CN 76 CN 82 CN 98 Year Rainfall, mm Discharge m³/s 2018 1686.04 23.73 24.43 35.24 2019 1872.67 27.27 27.95 39 2020 1688.86 23.2 23.95 35.1 2021 1871.05 27.87 28.6 40.19 2022 1734.01 22.22 22.61 35.58 Total 8840.55 200.19 127.56 282.96 Table 11 Total infiltration for the year 2018 to 2022 for CN 76, 82 and 98 . Curve Number CN 76 CN 82 CN 98 Year 2018 to 2022 Infiltration, mm 278.04 142.77 95.34 Table 12 Surface Runoff using SCS CN METHOD - Year 2018 to 2022 for CN 76, 82 and 98, (mm) CN 76 CN 82 CN 98 Year Rainfall, mm Surface Runoff, mm 2018 1686.04 81.72 87.16 106.54 2019 1872.675 111.86 135.67 193.48 2020 1688.863 48.40 55.80 82.10 2021 1871.055 143.25 159.50 214.29 2022 1734.01 94.49 123.04 144.76 The study employed the Soil Conservation Service Curve Number (SCS CN) method to calculate surface runoff for the designated area. Table 12 & Fig. 12 showcases the findings derived from the SCS CN Method and compares them with the results obtained from QSWAT. The examination demonstrates a congruent pattern between the plots generated by the SCS CN Method and the outcomes obtained through QSWAT for the same rainfall datasets 5. CONCLUSIONS In Marshall Stability tests, OGFC outperforms SG, while CRMB 55 exhibits a 43% increase in stability over VG 40, attributed to its high viscosity as a modified bitumen. Additionally, graphical comparisons show that increasing binder percentage reduces voids by 13% in CRMB 55 compared to VG 40, and as the number of blows increases in BC-2, VG 40 displays a significant 63% increase in voids compared to OGFC VG 40. The drain down test results indicate a 1.8% rise in drain down for CRMB 55 OGFC compared to VG 40 OGFC. The Tensile Strength Ratio shows a 15% increase in CRMB 55 OGFC over VG 40 OGFC, while the Cantabro Abrasion test demonstrates a 73% abrasion loss for VG40 OGFC compared to CRMB 55. Additionally, permeability is significantly higher in VG 40 OGFC compared to VG 40 SG, PMB 40, CRMB 55, and VG 40 BC-2, with differences of 21%, 10%, and 91% respectively. Increasing the number of blows decreases voids in VG40, PMB 40, and CRMB 55, with CRMB 55 showing fewer voids due to its high viscosity, while BC-2 typically exhibits the smallest voids compared to OGFC. This suggests that BC-2 formulations may have lower void content compared to VG40, influenced by factors such as binder modification and compaction practices. The adoption of pervious over impervious pavement reduced surface runoff and discharge by 34% from 2018 to 2022, while infiltration decreased by 65% over the same period. This demonstrates that as pavement becomes impervious, infiltration rates decrease. Among the nine vulnerable zones, the Kanakapura Valley in Bangalore City stands out as particularly critical. Within this area, I focused on a specific section to demonstrate the potential benefits of replacing Bituminous Concrete roads with porous asphalt as a means of alleviating urban flooding. Abbreviations VG 40 – Viscosity Grade 40, PMB 40 – Polymer Modified Bitumen 40, CRMB 55 (Crumb Rubber Modified Bitumen 55, BC-2 – Bituminous Concrete Grade 2, OGFC – Open Graded Friction Coarse, SG – Selected Grade, ITS – Indirect Tensile Strength, TSR- Tensile Strength Ratio, SWAT – Soil and Water Assessment Tool, CN – Curve Number, SCS – Soil Conversion Services Declarations Author Contribution The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.• All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yateen Lokesh, Dr. Harshad R Parate , Dr. H M Rajashekhar Swamy and Shreyas T . The first draft of the manuscript was written by Yateen Lokesh and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.• Authors declare that no funding was received for conducting this study.• The authors have no competing interests to declare that are relevant to the content of this article. Acknowledgement Authors are thankful to Ramaiah University of Applied Sciences, Bangalore for extending their support with the necessary facilities in conducting the research. Data Availability The authors declare that the data supporting the findings of this study are available. Upon request, the data can be provided by contacting the corresponding author (Mr. Yateen Lokesh). References Choudhary, R. et al. (2020). "A Study on Permeability Characteristics of Asphalt Pavements." 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Lokesh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYNCDD0hsHtzKmOEsxsYZJGtpxqMMAQyOnz/44OMOBnl+Bubjj21z6vINjh9/wPCjhkHGHJeWM8nMhjPPMBjObGBLbM7ddthyw5kcA8aeYww8lg04tBxIZpPmbWNIMDjAYwjUcsDA4EAOAwNvAwOPwQEcWs4/Zv/9F6jF/gD/x2bLbXUGBuefP2D8i0/LjWQ2ZkaQLQw8jM2M25gNDG4kGDDjs0XyxmNjyd42CcMZh9kMZ/ZuO2wgeeONwWGZYxI4tfCdT3z44WebjTx/e/ODDz+BDuM7n/7w4ZsaG3tcWhQg4hLI8cPAcAAsggPIN+CUGgWjYBSMglEABQCftViVlkvlIgAAAABJRU5ErkJggg==","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yateen","middleName":"","lastName":"Lokesh","suffix":""},{"id":294941527,"identity":"10f1bea2-6194-4fa8-b4c1-a4c166b7b901","order_by":1,"name":"Harshad R Parate","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Harshad","middleName":"R","lastName":"Parate","suffix":""},{"id":294941528,"identity":"78bca414-dc56-43ef-a987-09b4fa14450f","order_by":2,"name":"Rajashekhar Swamy H M","email":"","orcid":"","institution":"Global Academy Of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rajashekhar","middleName":"Swamy H","lastName":"M","suffix":""},{"id":294941530,"identity":"bda4aa0f-b7d5-4a97-a198-b26b19d59d7a","order_by":3,"name":"Shreyas T","email":"","orcid":"","institution":"M S Ramaiah University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shreyas","middleName":"","lastName":"T","suffix":""}],"badges":[],"createdAt":"2024-04-18 11:33:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4287453/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4287453/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55393410,"identity":"96e0401a-e888-4b4b-afa7-41a59bd2a904","added_by":"auto","created_at":"2024-04-26 16:22:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGraphical Comparison of Average Indirect Tensile Strength (ITS) test results (KPa)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/821a01586cd24340634cd641.jpg"},{"id":55393409,"identity":"853c13cc-d91a-4981-aee8-a9123e338d98","added_by":"auto","created_at":"2024-04-26 16:22:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGraphical Comparison of Average Tensile Strength Ratio (TSR) test results (%)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/f4a10ddaa51be95afb3f221a.jpg"},{"id":55393417,"identity":"d9473638-60fd-46f1-a871-cdc3393bdcb2","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Representation of Average Drain-Down Test results (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/aec15bbf21802f6843760fc7.jpg"},{"id":55393415,"identity":"51294864-bca6-4004-bc01-2aa4ee982323","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Representation of Average Cantabro Abrasion Test results (%)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/4633f66ee5450caa64369326.jpg"},{"id":55393420,"identity":"b6bda84b-cb6b-42e9-8709-037d65de295d","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Comparison of Relation between permeability coefficient and % Voids for SG, OGFC and BC-2 respectively\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/bcdb23d18cc480ac4b414cd4.jpg"},{"id":55393416,"identity":"8c1c2044-d590-434c-8e30-77062bded2ef","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":38444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQSWAT analysis for CN 76\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/3885afe3d83b75af52e4683f.jpg"},{"id":55393421,"identity":"eb5dfbfe-fb06-4795-8f37-b79d13d35d6d","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":42388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQSWAT analysis for CN 82\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/f98ca476ee7ddd2412997bb8.jpg"},{"id":55393412,"identity":"48009642-7fae-447d-90a1-d1527771ea21","added_by":"auto","created_at":"2024-04-26 16:22:01","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":40798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQSWAT analysis for CN 98\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/48656f3391bb78b920576e4e.jpg"},{"id":55393422,"identity":"1b66c02a-8d9e-4b55-b69c-0547c3de116b","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":61609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRainfall v/s Surface Runoff (mm) from Year 2018 to 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/b5d9b71d81576113ef372f9d.jpg"},{"id":55393411,"identity":"7288739f-cf02-4625-adef-b3015db5a7b1","added_by":"auto","created_at":"2024-04-26 16:22:01","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":59112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYear 2018 to 2022 Rainfall v/s Discharge (m³/s)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/ede1bf170216b464d7c4b6e7.jpg"},{"id":55393413,"identity":"b3848ef3-f226-4767-b004-ed4118b0dfdc","added_by":"auto","created_at":"2024-04-26 16:22:01","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":46378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDaily Infiltration for CN 76, 82 and 98 for Year 2018 to 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/433541688feaa1b404194f3e.jpg"},{"id":55393419,"identity":"ae2f9266-4043-4dc1-a93a-55bd58c2a8ad","added_by":"auto","created_at":"2024-04-26 16:22:02","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":57113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRainfall v/s Surface Runoff (mm) using SCS CN METHOD for Year 2018 to 2022\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/8fbb5491b2502e2cc270c7e8.jpg"},{"id":55393743,"identity":"1166af86-7e7d-4e3f-8b43-0a36397ef363","added_by":"auto","created_at":"2024-04-26 16:30:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1010230,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4287453/v1/a494b119-648e-46aa-8123-8018a9bffc59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation on Permeability Behavior of Open and Dense Graded Asphalt Mixes and Their Effect on Surface Runoff","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eConventional bituminous pavements are provided with a wearing course which is expected to be impervious and cater better riding comfort to the motor vehicle users. These wearing course are usually constructed using considerably superior quality of materials compared to the underlying layers. Due to rapid urbanization, the available natural land are usually altered and utilized to develop the infrastructure to meet the growing needs. Majority of the land gets covered by structures and paved for better transportation network and connectivity. This lately has deprived the potential of the natural surface to infiltrate the runoff water during monsoon seasons and to recharge the ground water. Such an anthropogenic change has led to sever floods in many areas and sever draught in other. Urban areas are usually the most effected regions especially during monsoon seasons where the streets are seen to be flooded disrupting the daily chores of the public dwelling in the region dependent on road networks for commutes. These urban floods are usually due to several factors like improper town planning, improper hydrological impact studies, unauthorized land acquisitions, construction floss, poorly planned, designed and connected stormwater drain system and lack of maintenance of these infrastructural facilities. Bituminous pavements being sensitive to moisture are forcibly exposed to such floods for a prolonged duration which greatly impacts their service life due to structural and functional distresses. Several road user deaths are reported in many cities due to urban floods and pavement failure during monsoon season. Similarly, along roads networks in such regions, with slightly high vehicle operating speeds, vehicles face poor visibility due to water splash and has led to sever life threatening accidents.\u003c/p\u003e \u003cp\u003eRainfall is a vital component of the Earth's water cycle, but excessive or poorly managed rainfall can have detrimental effects on infrastructure, particularly roads. The need for using advanced technology has significantly increased in order to mitigate these impacts.\u003c/p\u003e \u003cp\u003eUtilization and implementation of Porous Asphalt Concrete (PAC) along urban road networks could be one such management strategies which could be adopted to curb the detrimental effects of urban flooding. But it has its own limitations to be used on roads for motor vehicles to ply upon in terms of strength related capabilities and hence are used usually on vehicular parking areas. Several researches are still being conducted worldwide to make this technology viable and sustainable to be used in place of conventional wearing course bituminous mixes.\u003c/p\u003e \u003cp\u003ePermeability refers to the ability of a material to allow the passage or flow of fluids, such as liquids or gases, through its structure. In the context of PAC, permeability is a crucial property that plays a significant role in how this specialized pavement layer functions. Permeability is a fundamental property of PAC that allows it to effectively manage rainwater and its associated challenges. By enabling stormwater to infiltrate the road surface, porous asphalt not only helps mitigate flooding by channelizing the water along the designated path and medium, prevent erosion, and curb pollution but also promotes safer driving conditions and more sustainable urban environment.\u003c/p\u003e \u003cp\u003eSeveral researchers have conducted studies on this area about the permeability characteristics of PAC. (R. Choudhary et al, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, J. Chen et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, H. Xu et al,2016, M.N. Akhtar et al,2021). The results clearly demonstrate that both field and laboratory tests were conducted, and laboratory tests, in particular, offer a controlled environment facilitating careful monitoring and control of variables. In the laboratory permeability tests, loose mixtures were subjected to compaction using varying blows from a Marshall compactor. Notably, the compacted samples exhibited lower permeability compared to cores, attributable to the greater thickness of the compacted specimens. Specifically, the thickness of the compacted specimens ranged from 60 to 70 mm, while the cores of the samples varied between 20 to 40 mm in thickness. The pavement, as assessed through the laboratory tests, displayed an average permeability ranging from 4.16 cm/s to 5.18 cm/s using the NCAT method and from 0.66 cm/s to 0.89 cm/s using the ASTM method.\u003c/p\u003e \u003cp\u003eThe literature on analysis was studied on watersheds (Krishnan, G, 2018), which focused on the critical importance of managing water resources, especially in the context of increasing water demands, declining water supplies, and climate change. The article also mentions the use of SWAT in various regions of India for estimating runoff and surface yield. The specific study area, the Nethravathi river basin in Karnataka, India, is introduced. The article provides monthly model calibration and validation results, showcasing the performance of the SWAT model in simulating peak runoff. During calibration, the model simulated a maximum peak runoff of 1800 m\u0026sup3;/s compared to the observed value of 1650 m\u0026sup3;/s in August 1997. In the validation phase, the simulated peak runoff was 1600 m\u0026sup3;/s, while the observed value was 1390 m\u0026sup3;/s in August 2005. It reports an average annual rainfall of 3721 mm in the basin, with an estimated average annual runoff of 1130 mm, equivalent to approximately 30% of the average annual rainfall. (Madhusudan et al,2021), The approach involves dividing the watershed into 11 sub-watersheds and further subdividing them into 29 HRUs based on land use/land cover (LU/LC) maps, soil maps, and slope variations. The article provides information on the time periods for calibration and validation, with calibration performed from 2011 to 2015 and validation from 2016 to 2020. The article reports an initial R\u0026sup2; value of 0.396 before calibration, indicating that the model over predicted the Shimsha River's flow capability.\u003c/p\u003e \u003cp\u003e(Mohammad, Ansari, A, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). K, Shanbor and J, Manoj. (2017). The article outlines the objective of the current research, which is to estimate the runoff volume and sediment load of Duhok Dam Reservoir from the beginning of its operation in 1988 to 2011 using the SWAT model. It reports an average annual runoff of 14.3\u0026nbsp;million cubic meters (MCM) and an average annual sediment load of 124.6*10^3 tons for the period from 1988 to 2011. These results offer valuable insights into the hydrology of the Duhok Dam watershed over this time span. (J. Chen et al ,2017, H. Xu et al ,2016, Y. Yan et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), K, Jan et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The research results indicate that with an escalation in rainfall intensity, both surface runoff and permeability coefficient of the Open-Graded Friction Course (OGFC) mixture, characterized by 24.8% air voids, demonstrate an increase. The study findings suggest that the occurrence of freeze-thaw cycles can result in heightened water infiltration and erosion within the asphalt mixture, with a pronounced impact on open-graded mixtures.\u003c/p\u003e"},{"header":"2. OBJECTIVES","content":"\u003cp\u003eThe objectives of the present study was to:\u003c/p\u003e\n\u003col style=\"list-style-type: upper-roman;\"\u003e\n \u003cli\u003eInvestigate and compare the structural, functional and durability characteristics of SG, OGFC and BC-2 with VG-40, PMB-40 and CRMB-55 as binders in Marshall specimens, having constant pelletized cellulose fibers (0.3% by weight of aggregates) and induction sensitive steel fibers (5% by weight of aggregates) in all the mix categories during the study\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConduct flood simulation analysis, compare the behavior of SG, OGFC and BC-2 mixes as wearing course in mitigating floods at Koramangala valley of Bangalore city, India to determine surface runoff on pavements with SG, OGFC and BC-2 as wearing course using Curve Numbers (CN) method and QSWAT Software (with different CNs based on soil characteristics, which, in turn, depend on the coefficient of permeability)\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3. METHODOLOGY","content":"\u003cp\u003eThe study methodology comprised three main steps:\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation of Mixes\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eMarshall Specimens for Selected Grade (SG), Open Graded Friction Course (OGFC), and Bituminous Concrete (BC-2) were prepared. These mixes utilized different binders (VG-40, PMB-40, and CRMB-55) while maintaining a consistent proportion of pelletized cellulose fibers (0.3% by weight of aggregates) and induction-sensitive steel fibers (5% by weight of aggregates).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLaboratory Evaluation\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eStructural, functional, and durability characteristics of the mixes were assessed at the Optimum Binder Content (OBC). This involved conducting a thorough comparison to analyze their performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFlood Simulation Analysis\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFlood simulation was carried out using the Curve Number (CN) method and QSWAT software. Various curve numbers were applied based on the permeability attributes of the mixes under consideration.\u003c/p\u003e"},{"header":"4. RESULT ANALYSIS","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMarshall Specimens were prepared for Selected Grade (SG), Open Graded Friction Course (OGFC), and Bituminous Concrete (BC-2) using VG-40, PMB-40, and CRMB-55 binders. These specimens contained consistent proportions of pelletized cellulose fibers (0.3% by weight of aggregates) and induction-sensitive steel fibers (5% by weight of aggregates). The aim was to determine the Optimum Binder Contents (OBCs) and compare the Marshall properties of each mix. The results of these evaluations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarshall Properties (Average) of SG, OGFC and BC-2 at OBC with VG-40\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradation Mixes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003cp\u003e(50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelected Grade (SG) (50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003cp\u003e(75 Blows)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimum Binder Content, OBC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density of\u003c/p\u003e \u003cp\u003especimen, Gb (gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical Density, Gt\u003c/p\u003e \u003cp\u003e(gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of air voids in\u003c/p\u003e \u003cp\u003emix, Vv, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Volume of Bitumen\u003c/p\u003e \u003cp\u003eBinder, Vb, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids in Mineral\u003c/p\u003e \u003cp\u003eAggregates, VMA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids Filled with\u003c/p\u003e \u003cp\u003eBitumen, VFB, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected Stability, Kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e785.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e759.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1060.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarshall Properties (Average) of SG, OGFC and BC-2 at OBC with PMB-40\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradation Mixes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003cp\u003e(50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelected Grade (SG) (50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003cp\u003e(75 Blows)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimum Binder Content, OBC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density of\u003c/p\u003e \u003cp\u003especimen, Gb (gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical Density, Gt\u003c/p\u003e \u003cp\u003e(gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of air voids in\u003c/p\u003e \u003cp\u003emix, Vv, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Volume of Bitumen\u003c/p\u003e \u003cp\u003eBinder, Vb, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids in Mineral\u003c/p\u003e \u003cp\u003eAggregates, VMA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids Filled with\u003c/p\u003e \u003cp\u003eBitumen, VFB, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected Stability, Kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1244.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1230.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1675.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarshall Properties (Average) of SG, OGFC and BC-2 at OBC with CRMB-55\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGradation Mixes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003cp\u003e(50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSelected Grade (SG) (50 Blows)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003cp\u003e(75 Blows)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimum Binder Content, OBC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulk Density of\u003c/p\u003e \u003cp\u003especimen, Gb (gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical Density, Gt\u003c/p\u003e \u003cp\u003e(gm/cc)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume of air voids in\u003c/p\u003e \u003cp\u003emix, Vv, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Volume of Bitumen\u003c/p\u003e \u003cp\u003eBinder, Vb, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids in Mineral\u003c/p\u003e \u003cp\u003eAggregates, VMA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoids Filled with\u003c/p\u003e \u003cp\u003eBitumen, VFB, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected Stability, Kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1369.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1359.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1828.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow, mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 ITS and TSR:\u003c/h2\u003e \u003cp\u003eThe specimens prepared at the Optimum Binder Content (OBC) for the various mixes underwent laboratory testing, specifically the Indirect Tensile Strength Test (ITS) and Tensile Strength Ratio (TSR), in accordance with AASHTO-T-283 specifications. This allowed for the examination and comparison of their tensile strength characteristics and moisture sensitivity. The results of these tests are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, while their graphical representations can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Indirect Tensile Strength (ITS) test results (KPa)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVG 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnconditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e730.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e520.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e484.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePMB 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnconditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e662.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e527.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e568.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e578.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e448.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e526.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCRMB 55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnconditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e877.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e833.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e837.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConditioned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e787.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e717.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e809.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Tensile Strength Ratio (TSR) test results (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVG 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMB 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRMB 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe findings reveal that in the case of Selected Grade (SG) with VG 40 as the binder, the Tensile Strength Ratio (TSR) stood at 75.42%, while for Open Graded Friction Course (OGFC), it reached 78.17%, surpassing SG's TSR. Conversely, when PMB 40 was the binder, SG demonstrated a superior TSR of 85.07% compared to OGFC's 87.21%. However, with CRMB 55 as the binder, OGFC showcased a higher TSR of 89.76% compared to SG's TSR of 86.05%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Drain Down Test:\u003c/h2\u003e \u003cp\u003eIn \u003cem\u003eTable\u0026nbsp;6\u003c/em\u003e \u0026amp; \u003cem\u003eFig.\u0026nbsp;3\u003c/em\u003e, the Drain Down test reveals a drain down of 2.32% for Open Graded Friction Course (OGFC) and 3.25% for Selected Grade (SG) when VG 40 is used as the binder. The results indicate a 1.8% increase when CRMB 55 is compared to VG 40. According to IRC 129: 2019 standards, the maximum allowable value for Drain Down is 0.3. In this study, the values for OGFC exceed this limit, remaining at 2.32%, while SG, the selected grade, records 3.25%. However, it's noteworthy that SG's result is comparatively better than OGFC's and still complies with Indian Standards.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable 6: Average Drain-Down Test results (%)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVG 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePMB 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRMB 55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Cantabro Abrasion Test:\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is evident that the selected grade experiences a lower abrasion loss, at 6.45%, compared to the OGFC, which exhibits a higher abrasion loss of 7.25%. This comparison indicates that the selected grade outperforms the OGFC in terms of gradation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Cantabro Abrasion Test results (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVG 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePMB 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRMB 55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Permeability Test:\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it is evident that the permeability of VG 40 OGFC is 21% higher than that of VG 40 SG. Similarly, with PMB 40 and CRMB 55, the permeability of OGFC is 10% higher compared to SG. Furthermore, when compared to VG 40 BC-2, VG 40 OGFC exhibits a significantly higher permeability, with a staggering 91% difference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Permeability Test (cm/s) with Voids (%)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Voids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOGFC\u003c/p\u003e \u003cp\u003ecm/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Voids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003cp\u003ecm/s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% Voids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBC-2\u003c/p\u003e \u003cp\u003ecm/s\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVG 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePMB 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRMB 55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Simulation for Flood Analysis using QSWAT Software:\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis involved the estimation of surface runoff and discharge utilizing QSWAT Software and the SCS Curve Number method. For this study, Curve Numbers (CN) of 76, 82, and 98 were utilized. CN 76 and CN 82 were representative of pervious pavement, while CN 98 corresponded to BC-2 as represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;8 respectively.\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eIn accordance with the roster of susceptible locations recognized by the Bruhat Bengaluru Mahanagara Palike (BBMP) for the 2020-21 period, the study zone selected was the K-Valley (Koramangala Valley).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe composition of land usage within the research site, comprising 73.07% residential areas and 26.93% pavements, was derived from QGIS, encompassing a total area of 253.58 hectares.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe study encompassed estimations of surface runoff, discharge, and infiltration\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Surface Runoff:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates rainfall trends over a period of five years, demonstrating fluctuations in runoff across various Curve Numbers (CN). It is noteworthy that CN 76 shows reduced runoff, whereas CN 98 indicates higher runoff levels for similar rainfall intensities. This study emphasizes noticeable differences between CN 76 and CN 98. Notably, the shift from pervious surfaces represented by CN 76 to impervious pavement represented by CN 98 led to an increase in surface runoff from 4230.35 mm in 2018 to 6417.75 mm in 2022.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRainfall (mm) v/s Surface Runoff (mm) from Year 2018 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCN 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCN 82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCN 98\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall, mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSurface Runoff, mm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1686.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e796.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e821.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1204.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1872.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e915.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e941.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1332.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1688.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e775.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e803.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1198.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1871.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e933.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e963.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1373.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1734.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e737.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e757.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1216.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8840.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4230.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4287.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6417.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Discharge and Infiltration:\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e10\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the relationship between rainfall and discharge (measured in m\u0026sup3;/s) spanning five years, showcasing fluctuations in discharge levels among various Curve Numbers (CN). Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e11\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e11\u003c/span\u003e illustrates the infiltration process, revealing a decline in infiltration rates as CN values increase in reaction to rainfall. Notably, the shift from a pervious pavement with CN 76 to an impervious pavement with CN 98 led to a decrease in infiltration from 278.04 mm in 2018 to 95.34 mm in 2022.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRainfall (mm) v/s Discharge (m\u0026sup3;/s) from Year 2018 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCN 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCN 82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCN 98\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall, mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eDischarge m\u0026sup3;/s\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1686.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1872.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1688.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1871.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1734.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8840.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eTotal infiltration for the year 2018 to 2022 for CN 76, 82 and 98\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurve Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCN 82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCN 98\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e2018 to 2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInfiltration, mm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurface Runoff using SCS CN METHOD - Year 2018 to 2022 for CN 76, 82 and 98, (mm)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCN 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCN 82\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCN 98\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRainfall, mm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eSurface Runoff, mm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1686.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1872.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e193.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1688.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1871.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e159.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e214.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1734.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e123.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e144.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe study employed the Soil Conservation Service Curve Number (SCS CN) method to calculate surface runoff for the designated area. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e12\u003c/span\u003e \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e12\u003c/span\u003e showcases the findings derived from the SCS CN Method and compares them with the results obtained from QSWAT. The examination demonstrates a congruent pattern between the plots generated by the SCS CN Method and the outcomes obtained through QSWAT for the same rainfall datasets\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn Marshall Stability tests, OGFC outperforms SG, while CRMB 55 exhibits a 43% increase in stability over VG 40, attributed to its high viscosity as a modified bitumen. Additionally, graphical comparisons show that increasing binder percentage reduces voids by 13% in CRMB 55 compared to VG 40, and as the number of blows increases in BC-2, VG 40 displays a significant 63% increase in voids compared to OGFC VG 40. The drain down test results indicate a 1.8% rise in drain down for CRMB 55 OGFC compared to VG 40 OGFC.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Tensile Strength Ratio shows a 15% increase in CRMB 55 OGFC over VG 40 OGFC, while the Cantabro Abrasion test demonstrates a 73% abrasion loss for VG40 OGFC compared to CRMB 55. Additionally, permeability is significantly higher in VG 40 OGFC compared to VG 40 SG, PMB 40, CRMB 55, and VG 40 BC-2, with differences of 21%, 10%, and 91% respectively.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIncreasing the number of blows decreases voids in VG40, PMB 40, and CRMB 55, with CRMB 55 showing fewer voids due to its high viscosity, while BC-2 typically exhibits the smallest voids compared to OGFC. This suggests that BC-2 formulations may have lower void content compared to VG40, influenced by factors such as binder modification and compaction practices.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe adoption of pervious over impervious pavement reduced surface runoff and discharge by 34% from 2018 to 2022, while infiltration decreased by 65% over the same period. This demonstrates that as pavement becomes impervious, infiltration rates decrease.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAmong the nine vulnerable zones, the Kanakapura Valley in Bangalore City stands out as particularly critical. Within this area, I focused on a specific section to demonstrate the potential benefits of replacing Bituminous Concrete roads with porous asphalt as a means of alleviating urban flooding.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eVG 40 \u0026ndash; Viscosity Grade 40, PMB 40 \u0026ndash; Polymer Modified Bitumen 40, CRMB 55 (Crumb Rubber Modified Bitumen 55, BC-2 \u0026ndash; Bituminous Concrete Grade 2, OGFC \u0026ndash; Open Graded Friction Coarse, SG \u0026ndash; Selected Grade, ITS \u0026ndash; Indirect Tensile Strength, TSR- Tensile Strength Ratio, SWAT \u0026ndash; Soil and Water Assessment Tool, CN \u0026ndash; Curve Number, SCS \u0026ndash; Soil Conversion Services\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026bull; All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yateen Lokesh, Dr. Harshad R Parate , Dr. H M Rajashekhar Swamy and Shreyas T . The first draft of the manuscript was written by Yateen Lokesh and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026bull; Authors declare that no funding was received for conducting this study.\u0026bull; The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAuthors are thankful to Ramaiah University of Applied Sciences, Bangalore for extending their support with the necessary facilities in conducting the research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors declare that the data supporting the findings of this study are available. Upon request, the data can be provided by contacting the corresponding author (Mr. Yateen Lokesh).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChoudhary, R. et al. (2020). \"A Study on Permeability Characteristics of Asphalt Pavements.\" Transportation Research Board, 45, pp. 869\u0026ndash;882.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDr. Abdul, R. \u0026amp; Hussein, N. (2018). \"Comparing Laboratory and Field permeability of Modified Porous Asphalt Pavement.\" International Journal of Civil Engineering and Technology, 9(6), pp. 595\u0026ndash;600.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFwa, Lim, \u0026amp; Tan. (2015). \"Comparison of Permeability and Clogging Characteristics of Porous Asphalt and Pervious Concrete Pavement Materials.\" Journal of the Transportation Research Board, 2511(09), pp. 72\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J. et al. (2017). \"Investigation of permeability of open graded asphalt mixture considering effects of anisotropy and two-dimensional flow.\" Construction and Building Materials, 145, pp. 318\u0026ndash;325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, H. et al. (2016). \"Permeability of asphalt mixtures exposed to freeze\u0026ndash;thaw cycles.\" Cold Regions Science and Technology, 123, pp. 99\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarish. (2014). \"Low Cost Instrumentation for Testing Permeability of Bituminous Mixes.\" International Journal of Engineering Research \u0026amp; Technology (IJERT), 3(8), pp. 486\u0026ndash;489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhtar, M.N. et al. (2021). \"Stability and Permeability Characteristics of Porous Asphalt Pavement: An Experimental Case Study.\" Case Studies in Construction Materials, 126, pp. 426\u0026ndash;433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, Y. et al. (2016). \"Development of a predictive model to estimate permeability of dense-graded asphalt mixture based on volumetric.\" Construction and Building Materials, 126, pp. 426\u0026ndash;433.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammad, Ansari, A. \u0026amp; Knutson, S. (2016). \"Application of SWAT model to estimate the annual runoff and sediment of Duhok reservoir watershed.\" \u003cem\u003eScour and Erosion Proceedings\u003c/em\u003e, 42, pp. 1129\u0026ndash;1136.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadhusudhan et al. (2021). \"Application of SWAT model in estimating Surface Runoff values for Shimsha Watershed.\" International Journal of Engineering Development and Research (IJEDR), 9(3), pp. 9\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanbor, K. \u0026amp; Manoj, J. (2017). \"Rainfall-Runoff Modeling of a River Basin using SWAT Model.\" International Journal of Engineering Research \u0026amp; Technology (IJERT), 6(12), pp. 293\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhruvesh, P. \u0026amp; Nandhakumar, N. (2015). \"Runoff potential estimation of Anjana Khadi Watershed using SWAT model in the part of lower Tapi Basin, West India.\" Sustainable Water Resource Management, 12, pp. 1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, J. \u0026amp; Yang, C. (2020). \"Porous Asphalt Concrete: A Review of Design, Construction, Performance and Maintenance.\" International Journal of Pavement Research and Technology, 13, pp. 601\u0026ndash;612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKayhanian, M. (2018). \"Application of permeable pavements in highways for stormwater runoff management and pollution prevention: California research experiences.\" International Journal of Transportation Science and Technology, 4, pp. 1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJan, K. et al. (2017). \"The study of the effect of internal structure on permeability of porous asphalt.\" Road Materials and Pavement Design, 3, pp. 1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiyu, C. (2020). \"Influence of air void structures on the coefficient of permeability of asphalt mixtures.\" Powder Technology, 377, pp. 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLokesh, Y., Parate, H.R., \u0026amp; Swamy, H.M.R. (2022). \"Study on Effect of Porous Asphalt Pavements on Flood Mitigation for Bangalore City.\" International Journal of Multidisciplinary Research and Growth Evaluation, 3(2), 38\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanjana, Y.C., Nikhil, T.R., \u0026amp; Lokesh, Y. (2018). \"Performance Evaluation of Hot Mix Asphalt Using Modified Binders for Bituminous Concrete Grade-2.\" SSRG International Journal of Civil Engineering (SSRG \u0026ndash;IJCE), 5(9), 12\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopinath, R. \u0026amp; Lokesh, Y. (2015). \"Study on Strength and Physical Properties of Bituminous Concrete Grade-1 Mix, With E-Waste Ceramics as Replacement to Aggregates.\" Scientific Israel-Technological Advantages, 17(4), 184.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4287453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4287453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffectively managing stormwater runoff is crucial for minimizing the adverse impact of heavy rainfall events such as flooding and irrecoverable damages in asphalt pavements. To optimize stormwater management strategies and prevent flooding of roads and streets, it is essential to understand the structural, functional and durability behavior of both dense and open graded bituminous pavements under adverse rainfall conditions. The present study aimed to investigate a trial/Selected Grade (SG), Open Graded Friction Course (OGFC) as per IRC: 129-2019 and Bituminous Concrete Grade-2 (BC-2) with VG-40, PMB-40, and CRMB-55 as binders. Additionally, pelletized cellulose fibers (0.3% by weight of aggregates) and induction sensitive steel fibers (5% by weight of aggregates) were used only in SG and OGFC mixes for the study. The Marshall specimens prepared for the mentioned mixes were subjected to various laboratory tests to investigate and compare the structural, functional and durability characteristics. The laboratory results obtained were later used to compare, simulate and observe the behavior of the mentioned mixes to manage surface runoff and curb urban flooding using SCS Curve Number Method and QSWAT3-1.6.5 software. Koramangala in Bangalore city was selected as the potentially flood prone zone among 10 severely vulnerable zones and 19 moderately vulnerable zones for simulation based on Bruhat Bengaluru Mahanagara Palike’s (BBMP’s) urban flood report for the year 2020-21. The research findings indicates that BC-2 possessed 25.11% better Marshall Stability and 2.65% better Indirect Tensile Strength (ITS) compared to the open graded mixes (OGFC and SG) with CRMB-55 as binder. It was also observed that BC-2 had 7.11% more resistance to moisture compared to open graded mixes (OGFC and SG) with CRMB-55 as binder. On the contrary, among the open graded mixes, OGFC indicated 91.66% and 81.26% better permeability and abrasion resistance respectively compared to BC-2 mix. Based on the rainfall simulation conducted it was observed that the surface runoff on BC-2 mix using SCS Curve Number Method was 42.18% and using QSWAT3-1.6.5 software was 31.25% more than the open graded mixes considered for the study. The simulation also indicated that BC-2 mix had 30.07% more discharge than the open graded mixes. It was also observed that open graded mixes had 65.70% more infiltration compared to BC-2 mix. The research concludes that the open graded mixes are comparatively less strong than BC-2 mix but, on the contrary performs better in curbing urban floods and hence could be considered as a sustainable infrastructure solution.\u003c/p\u003e","manuscriptTitle":"Investigation on Permeability Behavior of Open and Dense Graded Asphalt Mixes and Their Effect on Surface Runoff","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 16:21:56","doi":"10.21203/rs.3.rs-4287453/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-15T07:57:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-11T02:40:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-02T10:27:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-01T09:33:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73740731654048014547728543109175718209","date":"2024-04-30T16:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230390155059822829081333869708246813213","date":"2024-04-29T14:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216068680754423603720230555994201659291","date":"2024-04-28T17:10:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-28T14:35:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T10:23:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-23T08:13:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Civil Engineering","date":"2024-04-18T11:32:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb71eb3a-bdbe-463e-aa67-f3d62a817bf0","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T07:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 16:21:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4287453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4287453","identity":"rs-4287453","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}