Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm

Wang, F.L., Chan, T.H.T., Thambiratnam, D.P., & Tan, A.C.C. (2013) Damage diagnosis for complex steel truss bridges using multi-layer genetic algorithm. Journal of Civil Structural Health Monitoring, 3(2), pp. 117-127.

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Abstract

Considerate amount of research has proposed optimization-based approaches employing various vibration parameters for structural damage diagnosis. The damage detection by these methods is in fact a result of updating the analytical structural model in line with the current physical model. The feasibility of these approaches has been proven. But most of the verification has been done on simple structures, such as beams or plates. In the application on a complex structure, like steel truss bridges, a traditional optimization process will cost massive computational resources and lengthy convergence. This study presents a multi-layer genetic algorithm (ML-GA) to overcome the problem. Unlike the tedious convergence process in a conventional damage optimization process, in each layer, the proposed algorithm divides the GA’s population into groups with a less number of damage candidates; then, the converged population in each group evolves as an initial population of the next layer, where the groups merge to larger groups. In a damage detection process featuring ML-GA, as parallel computation can be implemented, the optimization performance and computational efficiency can be enhanced. In order to assess the proposed algorithm, the modal strain energy correlation (MSEC) has been considered as the objective function. Several damage scenarios of a complex steel truss bridge’s finite element model have been employed to evaluate the effectiveness and performance of ML-GA, against a conventional GA. In both single- and multiple damage scenarios, the analytical and experimental study shows that the MSEC index has achieved excellent damage indication and efficiency using the proposed ML-GA, whereas the conventional GA only converges at a local solution.

Impact and interest:

4 citations in Scopus
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ID Code: 60909
Item Type: Journal Article
Refereed: Yes
Keywords: Structural vibration, Damage detection, Truss bridges, Genetic algorithm, Model strain energy, Optimization-based methods
DOI: 10.1007/s13349-013-0041-8
ISSN: 2190-5479
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > CIVIL ENGINEERING (090500) > Structural Engineering (090506)
Divisions: Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer-Verlag Berlin Heidelberg
Copyright Statement: The original publication is available at SpringerLink http://www.springerlink.com
Deposited On: 24 Jun 2013 01:24
Last Modified: 24 Mar 2014 09:23

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