锈蚀RC柱水平抗力的机器学习预测及参数敏感性分析

Machine learning prediction and parameter sensitivity analysis for lateral capacity of corroded RC columns

为实现地震作用下破坏模式未知锈蚀RC柱的水平抗力快速预测,基于机器学习(ML)方法对锈蚀矩形RC柱的剩余水平抗力开展研究。首先,确定ML模型的输入参数,建立锈蚀矩形RC柱数据集,并对输入参数之间的相关性进行分析;然后,选择6种代表性ML算法(SVM、KNN、ANN、CNN、RF和CatBoost),搭建锈蚀RC柱的水平抗力ML模型,并采用网格搜索或试错法对模型的超参数进行优化,基于5折-交叉验证确定了ML模型的最优超参数;最后,根据4个常用的回归评估指标(E_(MA)、E_(RMS)、E_(MAP)和R^(2))与测试集,对6种ML模型的预测精度进行评估,并基于性能优异的ML模型开展了输入参数重要性排序以及参数敏感性分析。结果表明:神经网络模型的预测性能普遍优于集成类或其他单一类模型,其中ANN模型的预测效果最佳,其预测值与试验值之比的均值和标准差分别为1.01和0.14;剪跨比对锈蚀矩形RC柱的水平抗力影响最大且较为显著,重要性占比可高达35%;矩形RC柱的水平抗力随着剪跨比、箍筋间距与截面有效高度比以及钢筋锈蚀率的增加呈减小趋势,而随着轴压比的增加呈先增大后趋于稳定或减小趋势。

To achieve rapid lateral capacity prediction of corroded RC columns without specific failure modes under seismic action,residual lateral capacity of corroded rectangular RC columns has been investigated based on machine learning(ML)methods.First,the input parameters for the ML model were determined,the dataset of corroded rectangular RC column was reorganized and correlations among the input parameters were analyzed.Then,six typical ML algorithms,including SVM,KNN,ANN,CNN,RF and CatBoost,were selected to establish the ML models for lateral capacity of corroded RC columns,and the hyper-parameters of ML models were optimized and determined using grid search or trial-and-error methods as well as 5-fold cross-validation.Finally,the predictive accuracy of the six ML models was evaluated based on four widely-used regression evaluation metrics(E_(MA),E_(RMS),E_(MAP)and R^(2))and testing set,and the importance ranking of input parameters and parameter sensitivity analysis were carried out based on the excellent ML models.Results show that the predictive performance of neural network models is generally better than that of ensemble models or other single models,in which ANN model is more satisfactory,and the mean and standard deviation of the ratio of its predicted result to experimental value are 1.01 and 0.14,respectively.Shear-span ratio has the highest and significant effect on the lateral capacity of corroded rectangular RC columns,with an importance of up to 35%.The lateral capacity of rectangular RC columns decreases with the increase of shear-span ratio,the ratio of stirrup spacing to section effective height and corrosion level,but increases at first and then tends to stabilize or decrease with the increase of axial load ratio.