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The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt’s mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (R2) for the Binder and Topeka were 0.93 and 0.97 in the Marshall’s direction and 0.96 and 0.95 for the Marshall’s opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by R2 of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and R2 of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy.
The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt’s mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (R2) for the Binder and Topeka were 0.93 and 0.97 in the Marshall’s direction and 0.96 and 0.95 for the Marshall’s opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by R2 of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and R2 of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy.
Currently, the viscoelastic properties of conventional asphalt cement need to be improved to meet the increasing demands caused by larger traffic loads, increased stress, and changing environmental conditions. Thus, using modifiers is suggested. Furthermore, the Sustainable Development Goals (SDGs) promote using waste materials and new technologies in asphalt pavement technology. The present study aims to fill this gap by investigating the use of pulverized oil palm industry clinker (POPIC) as an asphalt–cement modifier to improve the fatigue life of bituminous concrete using an innovative prediction approach. Thus, this study proposes an approach that integrates statistically based machine learning approaches and investigates the effects of applied stress and temperature on the fatigue life of POPIC-modified bituminous concrete. POPIC-modified bituminous concrete (POPIC-MBC) is produced from a standard Marshall mix. The interactions between POPIC concentration, stress, and temperature were optimized using response surface methodology (RSM), resulting in 7.5% POPIC, 11.7 °C, and 0.2 MPa as the optimum parameters for fatigue life. To improve the prediction accuracy and robustness of the results, RSM and ANN models were used and analyzed using MATLAB and JMP Pro, respectively. The performance of the developed model was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE). The study found that using RSM, MATLAB, and JMP Pro resulted in a comprehensive analysis. MATLAB achieved an R² value of 0.9844, RMSE of 3.094, and MRE of 312.427, and JMP Pro achieved an R² value of 0.998, RMSE of 1.245, and MRE of 126.243, demonstrating higher prediction accuracy and superior performance than RSM, which had an R² value of 0.979, RMSE of 3.757, and MRE of 357.846. Further validation with parity, Taylor, and violin plots demonstrates that both models have good prediction accuracy, with the JMP Pro ANN model outperforming in terms of accuracy and alignment. This demonstrates the machine learning approach’s efficiency in analyzing the fatigue life of POPIC-MBC, revealing it to be a useful tool for future research and practical applications. Furthermore, the study reveals that the innovative approach adopted and POPIC modifier, obtained from biomass waste, meets zero-waste and circular bioeconomy goals, contributing to the UN’s SDGs 9, 11, 12, and 13.
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