Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference
Structural damage identification can be considered as an optimization problem, by defining an appropriate objective function relevant to structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for str...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/FL180100196 http://hdl.handle.net/20.500.11937/91515 |
| _version_ | 1848765535519506432 |
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| author | Ding, Z. Li, Jun Hao, Hong |
| author_facet | Ding, Z. Li, Jun Hao, Hong |
| author_sort | Ding, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Structural damage identification can be considered as an optimization problem, by defining an appropriate objective function relevant to structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for structural damage identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable damage identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for structural damage identification. Significant measurement noise effect and modelling errors are considered. Damage identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable damage identification, indicating the proposed method is a promising approach for structural damage detection using data with significant uncertainties and limited measurement information. |
| first_indexed | 2025-11-14T11:36:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-91515 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:36:48Z |
| publishDate | 2019 |
| publisher | ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-915152023-05-04T06:51:10Z Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference Ding, Z. Li, Jun Hao, Hong Science & Technology Technology Engineering, Mechanical Engineering Improved Jaya algorithm Bayesian interference Sparse regularization Structural damage identification Uncertainty Measurement noise VARYING ENVIRONMENTAL-CONDITIONS BEE COLONY ALGORITHM UNCERTAINTIES FREQUENCY PARAMETER DIAGNOSIS Structural damage identification can be considered as an optimization problem, by defining an appropriate objective function relevant to structural parameters to be identified with optimization techniques. This paper proposes a new heuristic algorithm, named improved Jaya (I-Jaya) algorithm, for structural damage identification with the modified objective function based on sparse regularization and Bayesian inference. To improve the global optimization capacity and robustness of the original Jaya algorithm, a clustering strategy is employed to replace solutions with low-quality objective values and a new updated equation is used for the best-so-far solution. The objective function that is sensitive and robust for effective and reliable damage identification is developed through sparse regularization and Bayesian inference and used for optimization analysis with the proposed I-Jaya algorithm. Benchmark tests are conducted to verify the improvement in the developed algorithm. Numerical studies on a truss structure and experimental validations on an experimental reinforced concrete bridge model are performed to verify the developed approach. A limited quantity of modal data, which is distinctively less than the number of unknown system parameters, are used for structural damage identification. Significant measurement noise effect and modelling errors are considered. Damage identification results demonstrate that the proposed method based on the I-Jaya algorithm and the modified objective function based on sparse regularization and Bayesian inference can provide accurate and reliable damage identification, indicating the proposed method is a promising approach for structural damage detection using data with significant uncertainties and limited measurement information. 2019 Journal Article http://hdl.handle.net/20.500.11937/91515 10.1016/j.ymssp.2019.06.029 English http://purl.org/au-research/grants/arc/FL180100196 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD fulltext |
| spellingShingle | Science & Technology Technology Engineering, Mechanical Engineering Improved Jaya algorithm Bayesian interference Sparse regularization Structural damage identification Uncertainty Measurement noise VARYING ENVIRONMENTAL-CONDITIONS BEE COLONY ALGORITHM UNCERTAINTIES FREQUENCY PARAMETER DIAGNOSIS Ding, Z. Li, Jun Hao, Hong Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title | Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title_full | Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title_fullStr | Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title_full_unstemmed | Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title_short | Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference |
| title_sort | structural damage identification using improved jaya algorithm based on sparse regularization and bayesian inference |
| topic | Science & Technology Technology Engineering, Mechanical Engineering Improved Jaya algorithm Bayesian interference Sparse regularization Structural damage identification Uncertainty Measurement noise VARYING ENVIRONMENTAL-CONDITIONS BEE COLONY ALGORITHM UNCERTAINTIES FREQUENCY PARAMETER DIAGNOSIS |
| url | http://purl.org/au-research/grants/arc/FL180100196 http://hdl.handle.net/20.500.11937/91515 |