Wind-induced fragility of a monopole structure via Artificial Neural Network based surrogate analysis
Wind-induced fragility of a monopole structure via Artificial Neural Network based surrogate analysis
复制标题
DOI：
10.1016/j.engstruct.2022.115515
复制
发表时间：
2023-03
影响因子：
5.5
通讯作者：
Lei Zhang;L. Caracoglia
中科院分区：
文献类型：
--
作者：
Lei Zhang;L. Caracoglia
尽管蛮力蒙特卡罗（Brute-force Monte Carlo，BFMC）采样方法由于其鲁棒性和高效性而被广泛应用于基于性能的风工程（Performance-based Wind Engineering，PBWE）中，但对于涉及大型结构和大量不确定性的情况，大量重复随机实现所需的计算成本仍然过高。为了减轻计算负担，本通信提出了一种替代建模方法，基于人工神经网络（ANN），以评估量化风致脆弱性所需的概率积分。由此产生的人工神经网络模型形成一个计算上可行的输入和输出变量之间的替代，通过BFMC随机模拟获得的观测数据库的基础上。本文对一细长双塔结构在多方向混合风荷载作用下的脆弱性进行了初步研究。人工神经网络供电的替代结果显示出足够的准确性，同时大大减少了计算时间的BFMC方法的成本不到1%。它是有前途的代理人工神经网络建模中的PBWE框架。
Although brute-force Monte Carlo (BFMC) sampling has been commonly used in performance-based wind engineering (PBWE) due to its robustness and efficiency, the computing cost required by the considerable amounts of repetitious, stochastic realizations for situations that involve large structures and a multitude of uncertainties is still prohibitive. To alleviate the computational burden, this communication proposes a surrogate modeling approach, based on artificial neural networks (ANNs), to evaluate the probabilistic integral required to quantify wind-induced fragilities. The resultant ANN models form a computationally viable alternative between input and output variables, based on a database of observations obtained through BFMC stochastic simulations. A preliminary study is conducted to examine the fragility of a slender, monopole tower structure under the excitation of multidirectional, mixed-climate wind loads. The ANN-powered surrogate results show adequate accuracy while drastically reducing the computing time to less than 1% of the cost of BFMC approach. It is promising to incorporate the surrogate ANN modeling in a PBWE framework.