Performance evaluation of the artificial hummingbird algorithm in the problem of structural damage identification

Abstract

Recently, Structural Health Monitoring (SHM) has become a critical component of the maintenance and safety of lifeline infrastructures such as dams, skyscrapers, and bridges, thanks to its ability to detect structural failures at the early stages. In this paper, we evaluate the performance of the SHM damage identification tool using a novel metaheuristic algorithm called the Artificial Hummingbird Algorithm (AHA). The proposed approach is evaluated by two case studies of different bridge structures in Vietnam with different simulated damage scenarios. The potency of the AHA is compared against the other well-known metaheuristic algorithms such as Cuckoo Search (CS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning Based Optimization (TLBO). The results show that the AHA performs much better than the other algorithms in terms of accuracy and computational cost. The application of AHA can help to reduce the cost and time required for structural maintenance significantly, as well as improve the lifecycle of the structure.