Localizing and quantifying structural damage by means of a beetle swarm optimization algorithm

Abstract

An efficient meta-heuristic algorithm, named beetle swarm optimization (BSO), is proposed to localize and quantify structural damage using limited vibration measurement data. The beetle antennae search (BAS) algorithm that imitats a random walking mechanism in nature was recently developed to solve the optimization problem. However, the ratio of convergence of this algorithm significantly relys on the random direction and deviation for high-dimensional problems. To overcome this shortcoming, the BSO inspired by the swarm intelligence strategy is proposed. In the iterative search process of the BSO, each beetle swarm moves in a random direction like the BAS and the swarm of beetles is cognitive with the optimal one for the searching behavior. Consequently, the optimal one is updated step by step until a better beetle appears. To demonstrate the capability and robustness of the BSO, numerical and experimental studies using limited vibration measurement data of an offshore wind turbine structure are carried out for structural damage identification. An novel objective function is established by combining natural frequencies with mode shapes of the structure. The numerical results show that the BSO can accurately localize and quantify various types of damage even in a noise and temperature variations polluted environment. Moreover, it has higher accuracy and faster convergence speed than the BAS and the particle swarm optimization (PSO) algorithms. These promising performances could contribute to establishing a structural monitoring system for safety assurance of wind turbine structures.

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

“…Then the current optimal solution is updated. Through this approach, the BAS can continuously search for better solutions, thereby finding the optimal solution on a global scale [31][32][33][34][35][36]. The search principle and abstract model of the longhorn beetle are shown in Figure 3.…”

An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning

“…where c 1 and c 2 are positive numbers. r 1 and r 2 are random numbers ranging from 0 to 1. w is a weight value which is an adaptive number with the following Equation (9).…”

“…Singh et al [8] combined BSO algorithm to propose a heart disease and multi morbidity diagnosis model. Jiang et al [9] utilized the efficiency of BSO algorithm to localize and quantify structural damage. Zhou et al [10] put forward an improved BSO algorithm to obtain the shortest path and implementing intelligent navigation control for autonomous navigation robots.…”

Nonlinear hysteretic parameter identification using improved artificial bee colony algorithm

“…Aiming to overcome these difficulties, the swarmbased algorithms including the Le´vy tree-seed algorithm (Ding et al, 2019), the genetic algorithm and the artificial bee colony algorithm (Ding et al, 2016a(Ding et al, , 2017aKaraboga, 2005;Karaboga and Akay, 2009), Cukoo search algorithm (Xu et al, 2016), firefly algorithm (Zhou et al, 2019), beetle swarm optimization algorithm (Jiang et al, 2021) and so on are reasonably adopted because these algorithms are free from a careful choice of the initial parameters, do not require the gradient information and are able to find the global minima. Due to these benefits, the swarmbased algorithms have been widely applied to a number of nonlinear system parameter identification problems.…”