Structural collapse performance assessment has been at the center of many researchers’ interest due to complications of this phenomenon and uncertainties involved in modeling the simulation of the structural collapse response. This research aims to predict the structural collapse responses including mean collapse capacity, collapse standard deviation, and collapse drift by considering modeling uncertainties and then estimating collapse fragility curves, collapse risk, and reliability using Response Surface Method (RSM) and Artificial Neural Network (ANN). Modeling uncertainties for evaluating collapse responses are the parameters of the modified Ibarra-Krawinkler moment-rotation curve. Moreover, to analyze the structural uncertainty, the correlation between the model parameters in one component and between two structural components was considered. The Latin Hypercube Sampling (LHS) method and Cholesky decomposition were used to produce independent and dependent random variables, respectively. To predict the collapse responses of the structure, taking into account the uncertainties, as the number of uncertainties increases, the number of simulations for the uncertainties also increases, leading to a significant increase in the computational effort to estimate the structural responses, in the presence of a limited number of samples for uncertainties, a hybrid of ANN with PSO algorithm was used to reduce the computational effort in order to estimate the collapse fragility curves, collapse risk, and structural reliability. The results show that structural collapse responses can be predicted with appropriate accuracy by producing a limited number of samples for uncertainties and using an ANN-PSO algorithm.

由于这种现象的复杂性以及在模拟结构倒塌响应的模拟过程中所涉及的不确定性，结构倒塌性能评估一直是许多研究人员关注的焦点。这项研究旨在通过考虑模型不确定性来预测结构的倒塌响应，包括平均倒塌能力，倒塌标准偏差和倒塌漂移，然后使用响应面法（RSM）和人工神经网络（ANN）估算倒塌脆弱性曲线，倒塌风险和可靠性）。用于评估倒塌响应的模型不确定性是修正的Ibarra-Krawinkler矩-旋转曲线的参数。此外，为了分析结构不确定性，考虑了一个组件中的模型参数与两个结构组件之间的相关性。拉丁超立方体采样（LHS）方法和Cholesky分解分别用于产生独立和因变量。在考虑到不确定性的情况下，预测结构的倒塌响应，随着不确定性数量的增加，不确定性的模拟次数也随之增加，从而导致在存在结构时估算结构响应的计算量显着增加对于有限数量的不确定性样本，使用了ANN和PSO算法的混合来减少计算量，以估计倒塌脆弱性曲线，倒塌风险和结构可靠性。