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Author Wang, F. L. ♦ Chan, T. H. T. ♦ Thambiratnam, D. P. ♦ Tan, A. C. C.
Source SpringerLink
Content type Text
Publisher Springer-Verlag
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Technology ♦ Engineering & allied operations
Subject Keyword Structural vibration ♦ Damage detection ♦ Truss bridges ♦ Genetic algorithm ♦ Model strain energy ♦ Optimization-based methods ♦ Civil Engineering ♦ Measurement Science and Instrumentation ♦ Vibration, Dynamical Systems, Control
Abstract A 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, such as 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 and 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.
ISSN 21905452
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-03-21
Publisher Place Berlin, Heidelberg
e-ISSN 21905479
Journal Journal of Civil Structural Health Monitoring
Volume Number 3
Issue Number 2
Page Count 11
Starting Page 117
Ending Page 127

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Source: SpringerLink