Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique  

作　　者：Quynh-Anh Thi Bui Dam Duc Nguyen Hiep Van Le Indra Prakash Binh Thai Pham 

机构地区：[1]Civil Engineering Department,University of Transport Technology,54 Trieu Khuc,Thanh Xuan,Hanoi,100000,Vietnam [2]DDG(R)Geological Survey of India,Gandhinagar,382010,India

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期691-712,共22页工程与科学中的计算机建模（英文）

基　　金：the University of Transport Technology under grant number DTTD2022-12.

摘　　要：Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

关 键 词：Shear bond asphalt pavement grid search OPTIMIZATION machine learning 

分 类 号：TU528[建筑科学—建筑技术科学] TP3[自动化与计算机技术—计算机科学与技术]