The Application of Machine Learning Algorithms to Bond Strength between Steel Rebars and Concrete Using Bayesian Optimization

被引:1
|
作者
Yan, Huajun [1 ]
Xie, Nan [1 ]
Shen, Dandan [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China
[2] SANY Heavy Ind Co Ltd, Beijing 100044, Peoples R China
关键词
machine learning; Bayesian optimization; bond strength; Shapley additive explanation; EPOXY-COATED REINFORCEMENT; DEVELOPMENT LENGTH; SLIP BEHAVIOR; PULL-OUT; BARS; PREDICTION; STRESS; MODEL;
D O I
10.3390/ma17184641
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
The purpose of this study is to estimate the bond strength between steel rebars and concrete using machine learning (ML) algorithms with Bayesian optimization (BO). It is important to conduct beam tests to determine the bond strength since it is affected by stress fields. A machine learning approach for bond strength based on 401 beam tests with six impact factors is presented in this paper. The model is composed of three standard algorithms, including random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), combined with the BO technique. Compared to empirical models, BO-XGB`oost was found to be the most accurate method, with values of R2, MAE, and RMSE of 0.87, 0.897 MPa, and 1.516 MPa for the test set. The development of a simplified model that contains three input variables (diameter of the rebar, yield strength of reinforcement, concrete compressive strength) has been proposed to make it more convenient to apply. According to this prediction, the Shapley additive explanation (SHAP) can help explain why the ML-based model predicts the particular outcome it does. By utilizing machine learning algorithms to predict complex interfacial mechanical behavior, it is possible to improve the accuracy of the model.
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页数:17
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