Structural health monitoring of 3D frame structures using finite element modal analysis and genetic algorithm

Abstract

In this paper, we present a new application based on Genetic Algorithm (GA) to detect damage in 3D frame structures. Finite Element Method (FEM) is used to build models for intact and damaged structures. The identification of damage is formulated as an optimization problem using GA and the changes in natural frequencies. A 3D frame structure with two floor is used, as a numerical example, for damage identification. The proposed method is then applied to identify some removed elements. The results obtained using FEM is validated using experimental benchmark test of a 3D frame structure with eight floors. The results show that the proposed technique gives good damage identification compared with literature [1]. Furthermore, there is no error recorded in the prediction of location, however a small error is recorded in detecting damage severity. It is concluded that GA is an efficient tool to quantify single and multiple damages with high precision in 3D frame structures.

Investigation of Crack in Beam Structure using an Adaptive-Genetic Algorithm (AGA)

“…Discrete values for the geometry of the crack are taken to find the natural frequencies of the first three modes. There are works by different researchers, mainly Baviskar and Tungikar (2013), Tiachacht et al (2018), andAlexandrino, Gomes, andCunha (2020), have used different Artificial Intelligence (AI) and soft computing methods to train the data pool. Though different evolutionary algorithms use different coding systems and many of them use numerical values also.…”

تعیین مشخصات سازه با استفاده از پارامترهای مودال سازه و بکارگیری الگوریتم‌های بهینه‌سازی: ژنتیک، اجتماع ذرات و آموزش و یادگیری

“…The Teaching-learning-based Optimization exhibited the highest convergence rate and the lowest error compared to the Genetic Algorithm and Particle Swarm Optimization. For example, in the two-dimensional truss, the values of the objective function in the last iteration of the Genetic Algorithm, Particle Swarm Optimization, and Teaching-learning-based Optimization were 0.012 , 4 6 7 Modified total modal assurance criterion (MTMAC) 8 Grey Wolf Optimization (GWO) 9 Gradient-based Optimization 10 Slime mold algorithm 11 Marine predators algorithm 12 Ant lion optimizer 13 Whale optimization algorithm 14 Grasshopper optimization algorithm 15 Modal assurance criterion 16 Natural frequency vector assurance criterion 17 ( 2 )…”