Model updating method for detect and localize structural damage using generalized flexibility matrix and improved grey wolf optimizer algorithm (I-GWO)

Abstract

Abstract Various civil engineering-based infrastructures have been strategically planned to implement the structural health monitoring (SHM) system, considering their significance. A key objective faced by this system is the automatic identification and damage detection at the appropriate moment. Employing optimization algorithms in structural model updating is one approach to achieve this objective. This study’s main goal is to evaluate the location and extent of damage by combining two dynamically evolving parameters: the structure’s frequency and the generalized flexibility matrix. It is determined that the suggested approach produces more accurate and effective outcomes than the previous modal flexibility techniques. This is achieved by applying various noises and extracting the damaged structure’s data using the Improved Grey Wolf Optimizer (I-GWO). The accuracy of this method in locating the 15-story shear frame, the 25-member two-dimensional truss bridge, and the 23-member two-dimensional frame, as well as in identifying all damages, is demonstrated by the fact that the error between the simulated and estimated results in an average of twenty runs and each damage scenario was less than 3 percent. The findings demonstrate that the technique can precisely pinpoint the position and extent of damage in various structures, hence increasing the effectiveness of damage identification. Furthermore, they show that when compared to grey wolf optimizer (GWO) and particle swarm optimizer (PSO), I-GWO can offer a dependable method for precisely detecting damage.