Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques

Abstract

AbstractThe interlayer bonding condition in asphalt pavement significantly affects pavement performance. This study employed machine learning techniques to predict interlayer shear strength (ISS). Feed-forward artificial neural networks (ANN) and random forest (RF) models were developed and compared with traditional multiple linear regression (MLR). Utilizing 156 datasets, divided into 70% training and 30% testing, model performance was assessed using R-squared, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). SHapley Additive exPlanations (SHAP) was utilized for model interpretation. The results indicated that the ANN and RF models outperformed MLR, explaining over 95% of experimental data. RF exhibited superior performance with lowest MSE, RMSE, and MAE (0.0029, 0.0538, and 0.0376 MPa). SHAP analysis highlighted the significance of temperature, normal stress, shear deformation rate, and curing time as influential variables in ISS prediction. Elevated temperature adversely influenced ISS, while normal stress, shear deformation rate, and curing time positively contributed to ISS.KEYWORDS: Asphalt pavementinterlayer shear strengthmachine learningANNrandom forest (RF)SHAP AcknowledgmentsThis work was supported by the Fundamental Research Funds for the Central Universities, SWJTU [grant number 2682022CX002], National Natural Science Foundation of China [grant number 52278462], and Sichuan Youth Science and Technology Innovation Research Team (grant number 2021JDTD0023).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Fundamental Research Funds for the Central Universities, SWJTU [grant number 2682022CX002], National Natural Science Foundation of China [grant number 52278462], and Sichuan Youth Science and Technology Innovation Research Team (grant number 2021JDTD0023).