Data-driven models for predicting tensile load capacity and failure mode of grouted splice sleeve connection

Abstract

Grouted splice sleeve connection (GSSC) is an important connection technology in prefabricated structures, and the tensile load capacity and failure models of GSSC are very important to joint safety. In this study, two data-driven prediction methods (i.e., the machine learning (ML) method, and the threshold method) are proposed to predict the tensile load capacity and failure mode of GSSC. To this end, a database containing 418 existing GSSC experimental data is built. The database is used for eleven ML algorithms (i.e., linear prediction (LP), artificial neural network (ANN), support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), extremely randomized trees (ET), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost)) to establish ML models interpreted by shapley additive explanations (SHAP) and partial dependence plot (PDP). Further, the database is applied to the genetic programming (GP) algorithm to generate a simplified equation for the bond strength between rebar and grouting materials, which is the key mechanical parameter for the threshold method. The results show that both of these methods can effectively predict the tensile load capacity and failure mode of GSSC with various common construction defects, and the predictive performance of ML is slightly greater than that of the threshold method.