Structural damage assessment via model updating using augmented grey wolf optimization algorithm

Abstract

Some civil engineering-based infrastructures are planned for the structural health monitoring (SHM) system based on their importance. Identifiction and detecting damage automatically at the right time are one of the major objectives this system faces. One of the methods to meet this objective is model updating whit use of optimization algorithms in structures. This paper is aimed to evaluate the location and severity of the damage combining two being-updated parameters of the flexibility matrix and the static strain energy of the structure using augmented grey wolf optimization (AGWO) and only with extracting the data of damaged structure, by applying 5 percent noise. The error between simulated and estimated results in average of ten runs and each damage scenario was less than 3 percent which proves the proper performance of this method in detection of the all damages of the 37-member three-dimensional frame and the 33-member two-dimensional truss. Moreover, they indicate that AGWO can provide a reliable tool to accurately identify the damage in compare with the particle swarm optimizer (PSO) and grey wolf optimizer (GWO).