Advances in Structural Health Monitoring: Bio-Inspired Optimization Techniques and Vision-Based Monitoring System for Damage Detection Using Natural Frequency

Minkyu Jung, Jiyeon Koo and Andrew Jaeyong Choi ()
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Minkyu Jung: School of Computing, Department of AI-SW, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam 13306, Republic of Korea
Jiyeon Koo: School of Computing, Department of AI-SW, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam 13306, Republic of Korea
Andrew Jaeyong Choi: School of Computing, Department of AI-SW, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam 13306, Republic of Korea

Mathematics, 2024, vol. 12, issue 17, 1-21

Abstract: This paper introduces the improvements in natural-frequency-based SHM by applying bio-inspired optimization methods and a vision-based monitoring system for effective damage detection. This paper proposes a natural frequency extraction method using a motion magnification-based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including genetic algorithms, particle swarm optimization, sea lion optimization, and coral reefs optimization. To verify the performance of each bio-inspired optimization method, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each undamaged and damaged cantilever beam is extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer in damage detection is compared. As a result, coral reefs optimization shows the lowest average error, around 1%, in damage detection using the natural frequency.

Keywords: structural health monitoring; bio-inspired optimization; video motion magnification; vision-based damage detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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