Yan Huajun, Xie Nan, Shen Dandan
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.
SANY Heavy Industry Co., Ltd., Beijing 100044, China.
Materials (Basel). 2024 Sep 21;17(18):4641. doi: 10.3390/ma17184641.
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 R, 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.
本研究的目的是使用带有贝叶斯优化（BO）的机器学习（ML）算法来估计钢筋与混凝土之间的粘结强度。由于粘结强度受应力场影响，因此进行梁试验以确定粘结强度很重要。本文提出了一种基于401次梁试验和六个影响因素的粘结强度机器学习方法。该模型由三种标准算法组成，包括随机森林（RF）、支持向量回归（SVR）和极端梯度提升（XGBoost），并结合了BO技术。与经验模型相比，发现BO-XGBoost是最准确的方法，测试集的R、MAE和RMSE值分别为0.87、0.897MPa和1.516MPa。已提出开发一个包含三个输入变量（钢筋直径、钢筋屈服强度、混凝土抗压强度）的简化模型，以便于应用。根据此预测，Shapley加法解释（SHAP）有助于解释基于ML的模型为何预测出特定结果。通过利用机器学习算法预测复杂的界面力学行为，可以提高模型的准确性。
