Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise
One of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/3024209 |
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| author | Seung-Yong Ok Sungmoon Jung Junho Song |
| author_facet | Seung-Yong Ok Sungmoon Jung Junho Song |
| author_sort | Seung-Yong Ok |
| collection | DOAJ |
| description | One of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment. The goal of this paper is to expand the multiobjective optimization approach developed for robust damage identification in order to facilitate its applications to more realistic bridge damage identification problems. Specifically, a benchmark problem on highway bridges, developed under the auspices of International Association for Bridge Maintenance and Safety (IABMAS), is investigated. Various issues regarding sensor noises, multiple measurements, and loading scenarios are addressed to improve the robustness of bridge damage identification. A major finding from this study is that the stochastic process of Pareto optimal solutions obtained in a single run not only captures the actual damage locations successfully but also provides useful information such as damage-detected ratio on the potential candidates for damage to be inspected on site. Moreover, it is shown through the success, failure, and partial detection rates that the robustness of the proposed approach can be improved by using appropriate excitation scenarios and multiple sets of measurement data. |
| format | Article |
| id | doaj-art-937d51309d8044eba6df053fc96b759a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-937d51309d8044eba6df053fc96b759a2025-02-03T06:13:41ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/30242093024209Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor NoiseSeung-Yong Ok0Sungmoon Jung1Junho Song2Associate Professor, Department of Civil, Safety and Environmental Engineering, Hankyong National University, 327 Chungang-ro, Anseong-si, Kyonggi-do 17579, Republic of KoreaAssociate Professor, Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL 32310, USAProfessor, Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of KoreaOne of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment. The goal of this paper is to expand the multiobjective optimization approach developed for robust damage identification in order to facilitate its applications to more realistic bridge damage identification problems. Specifically, a benchmark problem on highway bridges, developed under the auspices of International Association for Bridge Maintenance and Safety (IABMAS), is investigated. Various issues regarding sensor noises, multiple measurements, and loading scenarios are addressed to improve the robustness of bridge damage identification. A major finding from this study is that the stochastic process of Pareto optimal solutions obtained in a single run not only captures the actual damage locations successfully but also provides useful information such as damage-detected ratio on the potential candidates for damage to be inspected on site. Moreover, it is shown through the success, failure, and partial detection rates that the robustness of the proposed approach can be improved by using appropriate excitation scenarios and multiple sets of measurement data.http://dx.doi.org/10.1155/2018/3024209 |
| spellingShingle | Seung-Yong Ok Sungmoon Jung Junho Song Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise Shock and Vibration |
| title | Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise |
| title_full | Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise |
| title_fullStr | Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise |
| title_full_unstemmed | Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise |
| title_short | Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise |
| title_sort | multiobjective optimization approach for robust bridge damage identification against sensor noise |
| url | http://dx.doi.org/10.1155/2018/3024209 |
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