An improved Artificial Rabbit Optimization for structural damage identification

Quyet Nguyen Huu; Tung Nguyen Xuan; Hoa Tran; Lan Nguyen Ngoc; Thanh Bui Tien; Hieu Nguyen Tran

doi:10.1590/1679-78257810

An improved Artificial Rabbit Optimization for structural damage identification

Authors

Quyet Nguyen Huu DX Laboratory, The University of Transportation and Communications Limited Company (UCT), Vietnam https://orcid.org/0000-0001-7186-4509
Tung Nguyen Xuan Structural Engineering Department, University of Transport and Communications https://orcid.org/0000-0003-3992-7746
Hoa Tran The Department of Bridge Engineering & Underground Infrastructure, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam https://orcid.org/0000-0003-2161-8064
Lan Nguyen Ngoc The Department of Bridge Engineering & Underground Infrastructure, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam https://orcid.org/0000-0002-0078-4074
Thanh Bui Tien The Department of Bridge Engineering & Underground Infrastructure, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam https://orcid.org/0000-0002-4001-9246
Hieu Nguyen Tran Faculty of Information Technology, University of Transport and Communications, Hanoi, Vietnam https://orcid.org/0000-0002-3007-6554

DOI:

https://doi.org/10.1590/1679-78257810

Abstract

This paper presents an enhanced version of the Artificial Rabbit Optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator-hunting characteristics into the prey-rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real-world applications in structural health monitoring and damage detection.

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Published

2024-01-09

Issue

Vol. 21 No. 1 (2024)

Section

Articles

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