基于ANN和XGB算法的锈蚀钢筋混凝土高温粘结强度预测方法

PREDICTION METHOD OF BOND STRENGTH OF CORRODED REINFORCED CONCRETE AT HIGH TEMPERATURE BASED ON ANN AND XGB ALGORITHM

为准确评估锈蚀钢筋混凝土(CRC)结构在突发火灾下的结构承载力,锈蚀钢筋混凝土高温粘结强度的统一预测方法研究亟待开展。然而,粘结退化机理复杂,粘结因素众多,实验方法不能考虑所有粘结因素的相关复杂关系的影响。在现有大量试验数据的基础上,采用机器学习方法可以有效地通过数据建立输入和输出特征之间的回归关系。该文利用ANN和XGB两种机器学习算法建立了一个统一的锈蚀钢筋混凝土高温粘结强度预测模型。基于612组高温锈蚀钢筋混凝土的试验研究数据,进行模型训练和测试。结果表明:ML模型的预测结果与实验结果十分吻合。此外,针对机器学习算法本身存在的黑盒子问题,使用SHAP方法来解决锈蚀钢筋混凝土高温粘结强度预测过程中的模型可解释性问题。同时,还将ML模型的计算结果与三种理论计算公式的结果进行了比较,结果表明:ML模型具有明显的优势。新构建的混合机器学习模型很有可能成为准确评估CRC结构经受高温后的损伤程度问题的新选择。

In order to accurately assess the structural bearing capacity of corroded reinforced concrete(CRC)structures under sudden fire incidents,there is an urgent need for research on a unified predictive method for the high-temperature bond strength of CRC.However,the degradation mechanism of bonding is complex,and there are numerous bonding factors.Experimental methods cannot consider the influence of all the complex relationships between bonding factors.Based on a large amount of existing experimental data,Machine Learning(ML)methods can effectively establish regression relationships between input and output features through data.In this study,two ML algorithms,artificial neural network(ANN)and extreme gradient boosting(XGB),were used to establish a unified predictive model for the high-temperature bond strength of CRC.The model was trained and tested upon 612 sets of experimental research data on high-temperature CRC.The results show that the predictions of the ML model are in a good agreement with the experimental results.In addition,to address the"black box"problem inherent in ML algorithms,the SHAP method was used to solve the interpretability problem during the prediction of high-temperature bond strength of CRC.Moreover,the calculation outcomes of the ML model were compared with those of three theoretical calculation formulas,and the ML model demonstrated clear advantages.The newly constructed hybrid ML model is likely to become a new choice for accurately assessing the extent of damage to CRC structures after exposure to high temperatures.