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Statistical Modeling of Asphalt Pavement Surface Friction Based on Aggregate Fineness Modulus and Asphalt Mix Volumetrics
https://orcid.org/http://orcid.org/0000-0001-9237-9959
Predicting pavement surface friction during the design stage allows engineers to optimize the design of the roadway to provide the appropriate level of friction for the intended use of the road in a safe and cost-effective manner. The main goal of the study is to propose a methodology to predict pavement surface friction during the design stage. Thus, this study analyzes the role of aggregate Fineness Modulus (FM) and Hot Mix Asphalt (HMA) volumetrics including Air Voids Volume (Va) and Effective Binder Volume (Vbe) on fabricating the surface texture. Surface frictional properties were evaluated using the British Pendulum Test (BPT) and the Sand Patch Test (SPT). The data were analyzed using the analysis of variance (ANOVA) test. Several statistical modeling techniques including Multiple Linear (ML) regression, Non-Linear Stepwise (NLSW) regression with all possible interactions, Non-Linear Beta (NLB) regression, Non-Linear Curve Fitting (NLCF) regression, and multilayer neural network (MNN) were utilized. Models were evaluated using synthetical data and compared using Post-Hoc analysis. The study evaluated nine types of mixes including different gradations with different Nominal Maximum Aggregate Sizes (NMAS) and several asphalt modifiers. The results revealed that Mean Textures Depth (MTD) and British pendulum Number (BPN) values are primarily influenced by FM, followed by Va and Vbe, respectively. According to ANOVA results, the two-level interaction showed that only when FM interacts with either Va or Vbe, the interaction is significant for both MTD and BPN. MNN models had the highest Coefficient of Determination (R2) values for both MTD and BPN. However, the sensitivity analysis and the Post-Hoc analysis revealed that due to the low number of data used to generate the models, statistical regression methods had comparable results and resulted in more accurate prediction than MNN. The NLCF was found to be the most reliable model for predicting both BPN and MTD.
Statistical Modeling of Asphalt Pavement Surface Friction Based on Aggregate Fineness Modulus and Asphalt Mix Volumetrics
https://orcid.org/http://orcid.org/0000-0001-9237-9959
Predicting pavement surface friction during the design stage allows engineers to optimize the design of the roadway to provide the appropriate level of friction for the intended use of the road in a safe and cost-effective manner. The main goal of the study is to propose a methodology to predict pavement surface friction during the design stage. Thus, this study analyzes the role of aggregate Fineness Modulus (FM) and Hot Mix Asphalt (HMA) volumetrics including Air Voids Volume (Va) and Effective Binder Volume (Vbe) on fabricating the surface texture. Surface frictional properties were evaluated using the British Pendulum Test (BPT) and the Sand Patch Test (SPT). The data were analyzed using the analysis of variance (ANOVA) test. Several statistical modeling techniques including Multiple Linear (ML) regression, Non-Linear Stepwise (NLSW) regression with all possible interactions, Non-Linear Beta (NLB) regression, Non-Linear Curve Fitting (NLCF) regression, and multilayer neural network (MNN) were utilized. Models were evaluated using synthetical data and compared using Post-Hoc analysis. The study evaluated nine types of mixes including different gradations with different Nominal Maximum Aggregate Sizes (NMAS) and several asphalt modifiers. The results revealed that Mean Textures Depth (MTD) and British pendulum Number (BPN) values are primarily influenced by FM, followed by Va and Vbe, respectively. According to ANOVA results, the two-level interaction showed that only when FM interacts with either Va or Vbe, the interaction is significant for both MTD and BPN. MNN models had the highest Coefficient of Determination (R2) values for both MTD and BPN. However, the sensitivity analysis and the Post-Hoc analysis revealed that due to the low number of data used to generate the models, statistical regression methods had comparable results and resulted in more accurate prediction than MNN. The NLCF was found to be the most reliable model for predicting both BPN and MTD.
Statistical Modeling of Asphalt Pavement Surface Friction Based on Aggregate Fineness Modulus and Asphalt Mix Volumetrics
https://orcid.org/http://orcid.org/0000-0001-9237-9959
Predicting pavement surface friction during the design stage allows engineers to optimize the design of the roadway to provide the appropriate level of friction for the intended use of the road in a safe and cost-effective manner. The main goal of the study is to propose a methodology to predict pavement surface friction during the design stage. Thus, this study analyzes the role of aggregate Fineness Modulus (FM) and Hot Mix Asphalt (HMA) volumetrics including Air Voids Volume (Va) and Effective Binder Volume (Vbe) on fabricating the surface texture. Surface frictional properties were evaluated using the British Pendulum Test (BPT) and the Sand Patch Test (SPT). The data were analyzed using the analysis of variance (ANOVA) test. Several statistical modeling techniques including Multiple Linear (ML) regression, Non-Linear Stepwise (NLSW) regression with all possible interactions, Non-Linear Beta (NLB) regression, Non-Linear Curve Fitting (NLCF) regression, and multilayer neural network (MNN) were utilized. Models were evaluated using synthetical data and compared using Post-Hoc analysis. The study evaluated nine types of mixes including different gradations with different Nominal Maximum Aggregate Sizes (NMAS) and several asphalt modifiers. The results revealed that Mean Textures Depth (MTD) and British pendulum Number (BPN) values are primarily influenced by FM, followed by Va and Vbe, respectively. According to ANOVA results, the two-level interaction showed that only when FM interacts with either Va or Vbe, the interaction is significant for both MTD and BPN. MNN models had the highest Coefficient of Determination (R2) values for both MTD and BPN. However, the sensitivity analysis and the Post-Hoc analysis revealed that due to the low number of data used to generate the models, statistical regression methods had comparable results and resulted in more accurate prediction than MNN. The NLCF was found to be the most reliable model for predicting both BPN and MTD.
Statistical Modeling of Asphalt Pavement Surface Friction Based on Aggregate Fineness Modulus and Asphalt Mix Volumetrics
Int. J. Pavement Res. Technol.
Alsheyab, Mohammad Ahmad (author) / Khasawneh, Mohammad Ali (author)
International Journal of Pavement Research and Technology ; 17 ; 1093-1111
2024-09-01
19 pages
Article (Journal)
Electronic Resource
English
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