替代模型
可靠性(半导体)
计算机科学
还原(数学)
有限元法
计算
帧(网络)
蒙特卡罗方法
采样(信号处理)
算法
重要性抽样
数学优化
人工智能
结构工程
机器学习
数学
工程类
电信
功率(物理)
统计
物理
几何学
滤波器(信号处理)
量子力学
计算机视觉
作者
Truong-Thang Nguyen,Manh-Hung Ha,Trong-Phu Nguyen,Viet-Hung Dang
标识
DOI:10.1177/13694332221092677
摘要
The reliability of building structures subjected to ground motion is a time-consuming, complex, and iterative computation problem involving multidiscipline theories such as probability theory, reliability theory, and dynamic structural analysis. In order to address this challenge, this study proposes a highly efficient approach based on the subset simulation and a deep learning-based surrogate model. The subset simulation is an efficient sampling strategy that significantly diminishes the number of samples to compute when determining the failure probability, especially for very small ones. On the other hand, the surrogate model based on a deep learning algorithm can deliver equivalently accurate structures’ responses compared to the well-known finite element method with markedly smaller time complexity, given appropriate available training data. The efficiency and effectiveness of the proposed approach are demonstrated in detail through three case studies with increasing complexity: a 1-DoF problem, a 2D frame, and a 3D structure based on experimental data showing a reduction up to two orders of magnitude in time complexity compared to the Monte Carlo simulation using finite element method.
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