Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques

Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R² (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent.

Laboratory Evaluation of the Use of Florida Washed Shell in Open-Graded Asphalt Mixtures

This study investigates the substitution of conventional aggregate with a Florida washed shell in open-graded asphalt mixtures and evaluates the optimal substitution percentage in aggregate gradations of various nominal maximum aggregate sizes (NMASs) (i.e., 4.75, 9.5, and 12.5 mm). Laboratory experiments were performed on open-graded asphalt mixture specimens with the coarse aggregate of sizes between 2.36 and 12.5 mm being replaced by the Florida washed shell at various percentages (0, 15, 30, 45, and 100%). Specimen properties relevant to the performance of open-graded asphalt mixtures in the field were tested, evaluated, and compared. Specifically, a Marshall stability test, Cantabro test, indirect tensile strength test, air void content test, and permeability test were conducted to evaluate the strength, resistance to raveling, cracking resistance, void content, and permeability of open-graded asphalt mixtures. The results show that there is no significant difference in the Marshall stability and indirect tensile strength when the coarse aggregates are replaced with Florida washed shell. This study also found that the optimum percentages of Florida washed shell in open-graded asphalt mixture were 15, 30, and 45% for 12.5, 9.5, and 4.75 mm NMAS gradations, respectively.

Laboratory Research and Evaluation on Design and Application Performance of High-Performance Cold-Mix Resin

To improve the safety of orthotropic steel bridge decks and the construction efficiency of bridge deck pavement by enhancing the performance of pavement materials, a new-generation, high-performance cold-mix resin was prepared by carrying out the combination of micro-characteristic analysis and performance test. Meanwhile, the pavement performance and fatigue performance of high-performance cold-mix resin mixtures and hot-mix epoxy saphalt mixtures as a control group were studied experimentally. The results show that different kinds of epoxy resins show bisphenol structure in essence. The curing exothermic peak temperature of the cold-mix resin increases with the heating rate. Both the specific heat capacity (△C_P) of cold-mix resin and cold-mix resin asphalts exhibit a sudden change between −20 °C and 40 °C. In resin asphalt mixtures, cold-mix resin forms the network structure skeleton whereas the asphalt distributed in the form of tiny particles. The dosage of respective component has a significant effect on the tensile strength and elongation at break of cold-mix resin. Compared with hot-mix epoxy asphalt mixtures, cold-mix resin mixtures exhibit comparable water stability and high and low-temperature performance, as well as greater fatigue life.