钢筋混凝土框架结构震损预测模型参数敏感性

Parameter sensitivity of the earthquake damage prediction model for an RC frame structure

应用机器学习算法进行钢筋混凝土框架结构地震易损性评估时,结构特征参数冗杂且无法便捷选取,针对此问题本文采用基于神经网络的敏感性分析方法,利用2个训练有素的神经网络模型,分别从结构层次和构件层次探究了不同输入参数对结构震损指标的影响大小。进行敏感性分析的参数包括5个几何参数(结构层数、标准层高度、X向跨度、X向跨数、Y向跨数)、2个设计参数(抗震设防烈度、场地类别)和1个地震动参数(地面峰值加速度)。结果表明:平面尺寸参数对结构及构件层次的震损指标敏感性均较小。剔除敏感性较小的参数后,在重要震损指标的预测上仍然有较高的准确性,为钢筋混凝土框架结构的震损预测提供了更简便的参数输入依据。

In this paper,to resolve the problem of the redundant and inconvenient selection of the structural characteristic parameters for the seismic vulnerability assessment of reinforced concrete frame structures by machine learning algorithms,a neural network-based sensitivity analysis method is employed to investigate the effect of different input parameters on structural damage indicators at the structural and component levels,using two well-trained neural network models.The parameters used for sensitivity analysis include five geometric parameters(the number of structural layers,the height of the standard layer,X-direction span,the number of X-direction spans,and the number of Y-direction spans),two design parameters(seismic intensity and site category),and one ground motion parameter(peak ground acceleration).The results show that the plane geometry parameters of a structure are less sensitive to seismic damage indicators at the levels of structure and components.After the less sensitive parameters are excluded,the prediction accuracy for important seismic damage indicators remains high,providing a simpler parameter input basis for the seismic damage prediction of an RC frame structure.