Abstract
This paper describes a new global damage identification framework for the continuous/periodic monitoring of civil structures. In order to localize and estimate the severity of damage regions, a one-stage model-based Bayesian probabilistic damage detection approach is proposed. This method, which is based on the response power spectral density of the structure, enjoys the advantage of broadband frequency information and can be implemented on input-output as well as output-only damage identification studies. A parallel genetic algorithm is subsequently used to evolve the optimal model parameters introduced for different damage conditions. Given the complex search space and the need to perform multiple time-consuming objective function evaluations, a parallel meta-heuristic provides a robust optimization tool in this domain. It is shown that this approach is capable of detecting structural damage in both noisy and noise-free environments.
Original language | English |
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Title of host publication | Structural Health Monitoring |
Subtitle of host publication | Research and Applications |
Number of pages | 11 |
DOIs | |
Publication status | Published - 26 Jul 2013 |
Externally published | Yes |
Event | Asia-Pacific Workshop on Structural Health Monitoring 2012 - RACV Club, Melbourne, Australia Duration: 5 Dec 2012 → 7 Dec 2012 Conference number: 4th https://eprints.usq.edu.au/22740/8/SHM2012.pdf (Call for Papers) |
Publication series
Name | Key Engineering Materials |
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Volume | 558 |
ISSN (Print) | 1013-9826 |
Workshop
Workshop | Asia-Pacific Workshop on Structural Health Monitoring 2012 |
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Abbreviated title | APWSHM 2012 |
Country/Territory | Australia |
City | Melbourne |
Period | 5/12/12 → 7/12/12 |
Other | Proceedings were published in Key Engineering Materials: Structural Health Monitoring: Research and Applications scientific.Net |
Internet address |
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Keywords
- Bayesian probabilistic approach
- Damage identification
- Parallel genetic algorithm
- Power spectral density
- Severity