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

Long Nguyen Ngoc, Quyet Nguyen Huu, Lan Nguyen Ngoc, Hieu Nguyen Tran
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引用次数: 1

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.
人工蜂鸟算法在结构损伤识别问题中的性能评价
近年来,结构健康监测(SHM)已成为生命线基础设施(如水坝、摩天大楼和桥梁)维护和安全的关键组成部分,因为它能够在早期阶段检测结构故障。在本文中,我们使用一种称为人工蜂鸟算法(AHA)的新型元启发式算法来评估SHM损伤识别工具的性能。通过越南不同桥梁结构的两个案例研究,对所提出的方法进行了评估。AHA的效力与其他知名的元启发式算法(如布谷鸟搜索(CS),遗传算法(GA),粒子群优化(PSO)和基于教学的优化(TLBO))进行了比较。结果表明,该算法在准确率和计算成本方面都优于其他算法。AHA的应用可以显著降低结构维护所需的成本和时间,并提高结构的生命周期。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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