PDF(2164 KB)
基于机器学习的砖砌体房屋震害快速预测方法
刘丽, 沈俊凯, 张令心
PDF(2164 KB)
PDF(2164 KB)
基于机器学习的砖砌体房屋震害快速预测方法
A Machine Learning-Based Method for Rapid Prediction of Earthquake Damage in Brick Masonry Houses
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由于现有的震害预测方法不能对砖砌体结构做出高效的预测，基于机器学习模型，提出了一种综合考虑地震动特性与结构特性的砖砌体结构震害快速预测方法.该方法利用机器学习模型，从时域、频域、反应谱和持时4个方面初步选取了能够代表地震动特性的12个参数，从承载力、刚度等方面初步选取了与砖砌体结构破坏相关性较强的7个结构参数；将地震动参数与结构参数相结合作为输入变量，分别给出了基于支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）三种机器学习模型的砖砌体结构的震害快速预测方法，并进行了性能比较；采用相关性分析对输入参数进行进一步优化，给出了优化输入参数后的最优预测模型.结果表明，当采用19个输入参数时，ANN模型的预测准确率最高，达到91.56%.当采用优化后的12个参数作为输入时，基于RF模型的预测性能更加稳定，预测的准确率也更高，可达到90.01%.优化输入参数后的基于RF模型的预测方法可以实现对砖砌体结构震害的快速预测；与只考虑结构参数或只考虑地震动参数作为输入的方法相比，同时考虑结构和地震动参数作为输入的方法极大地提高了预测的准确性.
Since the existing earthquake damage prediction methods cannot make rapid predictions for brick masonry structures. A rapid prediction method for earthquake damage of brick masonry structures is proposed. The method uses a machine learning model, considering the ground motion characteristics and structural characteristics. 12 ground motion parameters that represent the ground motion characteristics and 7 structural parameters that have a strong correlation with the damage of brick masonry structures are selected. The ground motion parameters are considered in four aspects: time domain, frequency domain, response spectrum and holding time, and the structural parameters are considered in terms of bearing capacity and stiffness. Three machine learning models based on support vector machine, random forest and artificial neural network are given for fast prediction of seismic damage of brick masonry structures. The input parameters were further optimized using correlation analysis, and the optimal model after optimizing the input parameters was given. The results show that the ANN model has the highest prediction accuracy of 91.56% when 19 input parameters were used. The prediction accuracy of the RF model-based earthquake damage prediction method was higher when 12 optimized parameters were used as inputs, reaching 90.01%. The prediction performance of the RF-based model was more stable when the input parameters were gradually reduced. The optimized input parameters of the RF model-based prediction method can achieve rapid prediction of seismic damage to brick masonry structures. The method that considers both structural and ground vibration parameters as input greatly improves the accuracy of prediction compared to the method that considers only structural parameters or only ground vibration parameters as input.
机器学习 / 震害快速预测 / 砖砌体结构 / 地震动特性 / 工程地质
machine learning / rapid prediction of earthquake damage / brick masonry structure / ground motion characteristics / engineering geology
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