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 

作者机构：Civil Engineering DepartmentUniversity of Transport Technology54 Trieu KhucThanh XuanHanoi100000Vietnam DDG(R)Geological Survey of IndiaGandhinagar382010India 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2025年第142卷第1期

页      面：691-712页

核心收录：

学科分类：08[工学] 081304[工学-建筑技术科学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0813[工学-建筑学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Shear bond asphalt pavement grid search optimization machine learning 

摘      要：Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement *** 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 ***,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 were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat *** 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 *** show that these models accurately predict SBS,with LGBM providing outstanding ***(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on ***,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.