Damage diagnosis in steel structures with different noise levels via optimization algorithms

Abstract

In this paper, the main objective is to solve the inverse problem of damage identification with the help of the Imperialist Competitive Algorithm (ICA) optimization. Three different numerical cases, including a clamped-free beam, a 2D truss and a 2D plate-type structure are modelled by using Finite Element Method (FEM) and used to evaluate the proposed damage identification procedure. The proposed objective function for the optimization procedure is formed by using modal parameters. Those parameters are obtained from the damage state, where cracks are simulated with the assumption of reducing the local stiffness of the structure. Then, proposed damage detection/characterization process is performed by implementing an optimization algorithm, called Imperialist Competitive Algorithm (ICA). Results obtained from the numerical case studies show that this algorithm is trustworthy and can be used to identify the severity and location of the damage with a good accuracy. Furthermore, the effects of noise on the results of damage identification process are studied so as to investigate the tolerance of the method in the face of environmental noise. Finally, the results obtained by the ICA are compared to the ones obtained by using two commonly used algorithms, i.e. the binary genetic algorithm (BGA) and particle swarm optimization (PSO).