Performance of Ant Lion Optimization and Artificial Bee Colony Algorithm for Structural Health Monitoring of ASCE Benchmark Structure

Abstract

Structural health Monitoring (SHM) has been a fast-moving tread for monitoring the health status of civil engineering structures. The SHM strategy involves application of different techniques involving the use of modal parameters, such as natural frequencies and mode shapes, to detect and localize the damage. Though localization of damage in structure plays an important role, damage quantification, which helps in performing repair works, is also essential. However, very few algorithms have been developed which calculates the amount of damage in structure. In view to this situation, the swarm-based optimization algorithms have been developed which can detect the damage severity in a structure. Some of the algorithms developed so far are Grey Wolf Algorithm (GWO), Artificial Bee Colony (ABC) algorithm, Firefly algorithm, Ant Lion Optimization (ALO) algorithm and others. Out of these algorithms, ALO and ABC have been used in limited number of cases for performing SHM. No real-life structures, considering the effect of noise, has been analysed using ALO and ABC. In this paper, the ALO and ABC optimization algorithm has been studied for damage analysis using an objective function based on the eigen value problem. The structure chosen for analysis is the ASCE Benchmark building which is a quarter scale model of an original building which give the real-life sense of SHM. Damage considered in the structure is of less severity occurring at multiple locations under an external noise, which is also a real-life challenge for damage analysis. The experimental results show that both ABC and ALO algorithms are able to quantify both major and minor damages in the presence of moderate amount of noise, proving the robustness of these algorithms.