Wind-induced fragility of a monopole structure via Artificial Neural Network based surrogate analysis. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhang, Lei; Caracoglia, Luca
• Is Part Of: Engineering structures. Volume 278(2023)
• Publication Date: 2023-03-01
• Subjects: Surrogate modeling -- Artificial neural networks -- Fragility analysis -- Slender monopole structure; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.115515
• Languages: English
• ISSNs: 0141-0296

Abstract

Abstract: 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.