Structural Damage Identification through an Optimal Sensor Placement Approach and the Modal Strain Energy Based Index
In this study, an efficient method for determining the accurate location of damage to structures is introduced using an optimal sensor placement (OSP) and modal strain energy-based index (MSEBI). The research is implemented in two main stages. In the first stage, a correlation function between the r...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Semnan University
2024-02-01
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| Series: | Journal of Rehabilitation in Civil Engineering |
| Subjects: | |
| Online Access: | https://civiljournal.semnan.ac.ir/article_7936_ae52bb46d2720deeba8b0768bb3a4b2a.pdf |
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| Summary: | In this study, an efficient method for determining the accurate location of damage to structures is introduced using an optimal sensor placement (OSP) and modal strain energy-based index (MSEBI). The research is implemented in two main stages. In the first stage, a correlation function between the reconstructed mode shapes using the iterated improved reduced system (IRS) method and the complete mode shapes of a structure is defined and then the function is minimized via the binary differential evolution (BDE) algorithm to find the optimal sensor placement of the structure. In the second stage, the location of damage is determined using MSEBI based on the optimum place of sensors obtained already. In order to assess the efficiency of the proposed method, two standard examples, including a two-dimensional (2-D) frame structure with 45 elements and a 2-D truss with 47 elements, are examined. Numerical results, considering different conditions, demonstrate that the integration of OSP method and MSEBI can provide an efficient tool for accurate and rapid identification of the damage location. The parametric study shows that the proposed method has a low sensitivity to the number of modes and noise level, and it can properly identify damage by considering a few modes and the high level of noise. |
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| ISSN: | 2345-4415 2345-4423 |