1- Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran, Iran
2- Department of Civil Engineering, University of Guilan, Rasht, Iran
3- Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran, Iran ,moghadas@aut.ac.ir
2- Department of Civil Engineering, University of Guilan, Rasht, Iran
3- Department of Civil & Environmental Engineering, Amirkabir University of Technology, Tehran, Iran ,
Abstract: (112 Views)
Moisture damage in asphalt mixtures poses significant challenges to infrastructure durability, necessitating accurate modeling for effective mitigation strategies due to the complex nature of moisture susceptibility. Current tests, such as those utilizing general indicators like the indirect tensile strength ratio, examine moisture susceptibility in asphalt mixtures. However, these tests incur substantial costs and require considerable time. Therefore, this study aims to develop moisture susceptibility prediction models using Multi-Gene Genetic Programming (MGGP). The research utilized four types of aggregates (two limestone and two granite types) and eight different Performance Grade (PG) bitumen types. The modified Lottman test method (AASHTO T283) was employed for moisture susceptibility assessment, with samples subjected to specific conditioning protocols including vacuum saturation (13-67 kPa absolute pressure), freeze-thaw cycles (-18°C for 16 hours), and hot water conditioning (60°C for 24 hours). Indirect tensile strength tests were conducted under controlled loading conditions (2 Hz frequency, 0.1s loading time, 0.4s rest period) at 25°C. The dataset comprised 34 samples and 11 variables to predict two key indicators: Inflection Stripping Point (ISP) and Stripping Slope (SS). The MGGP model demonstrated remarkable performance in predicting both ISP and SS, achieving R2 values of 0.981 and 0.974 for the test data, respectively. Several crucial parameters were analyzed, including the apparent film thickness (AFT) calculated using aggregate specific surface area, permeability measured through falling head test method (ASTM PS 129-01), and surface free energy components. The surface energy analysis incorporated both cohesive free energy (CFE) and adhesive free energy (AFE), with special attention to the acid-base theory components: Lifshitz-van der Waals (LW), Lewis acid (Γ+), and Lewis base (Γ-) components. For ISP prediction, the MGGP model identified key variables including the ratio of base to acid surface free energy (SFE), asphalt-water adhesion (ΓAsphalt-Water), cohesive free energy (CFE), adhesive free energy (AFE), permeability of asphalt mixture (PAM), asphalt film thickness (AFT), and degree of saturation (DS). The model for SS prediction emphasized the importance of ΓAsphalt-Water, aggregate-water adhesion (ΓAggregate-Water), wettability, specific surface area (SSA), PAM, and DS. The study employed various performance metrics to evaluate the MGGP models. For ISP predictions, the model achieved RMSE, MSE, and MAE values of 5.228, 27.337, and 3.843, respectively. For SS predictions, these values were 0.294, 0.086, and 0.231, respectively, indicating high accuracy and low error rates. These results surpass those of previous studies employing traditional Genetic Programming (GP) methods, highlighting the potential of MGGP as a powerful tool in modeling asphalt moisture susceptibility. The practical implications of this research are significant for improving asphalt mixture durability, reducing maintenance costs, and enhancing road safety. Future research could focus on validating the models across a broader range of asphalt mixtures and environmental conditions.
Keywords: Asphalt Mixture, Moisture Susceptibility, Machine Learning, Multi-Gene Genetic Programming
Article Type: Original Research |
Subject:
Roads and Transportation
Received: 2024/10/25 | Accepted: 2025/03/11
Received: 2024/10/25 | Accepted: 2025/03/11
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