Finite element model updating and damage identification using semi-rigidly connected frame element and optimization procedure: An experimental validation
Article
Ghannadi, Parsa, Khatir, Samir, Kourehli, Seyed Sina, Nguyen, Andy, Boutchicha, Djilali and Wahab, Magd Abdel. 2023. "Finite element model updating and damage identification using semi-rigidly connected frame element and optimization procedure: An experimental validation." Structures. 50, pp. 1173-1190. https://doi.org/10.1016/j.istruc.2023.02.008
Article Title | Finite element model updating and damage identification using semi-rigidly connected frame element and optimization procedure: An experimental validation |
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ERA Journal ID | 211389 |
Article Category | Article |
Authors | Ghannadi, Parsa, Khatir, Samir, Kourehli, Seyed Sina, Nguyen, Andy, Boutchicha, Djilali and Wahab, Magd Abdel |
Journal Title | Structures |
Journal Citation | 50, pp. 1173-1190 |
Number of Pages | 18 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 2352-0124 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.istruc.2023.02.008 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2352012423001662 |
Abstract | Owing to ever-increasing complexity of engineering structures, developing a methodology for the early detection of defects has become crucial to ensure their long-term safety and reliability with the least amount of expense. There are always discrepancies between experimental and numerical modal data because of unknown structural parameters and uncertainties. The finite element (FE) model updating techniques attempt to minimize the differences by adjusting the unknown parameters of the FE model. Therefore, the FE model updating methods are essential for developing a baseline model and accurate damage identifications in subsequent steps. This paper employs the semi-rigidly connected frame element (S-RCFE) instead of the standard Euler-Bernoulli beam element for assembling the FE model of the experimental beam and establishing a high-fidelity numerical model. The S-RCFE with extra design parameters, including the end fixity factor of all connections, enables us to achieve a reasonable agreement between experimental and numerical models through the optimization-based procedure. In FE model updating step, two objective functions based on modified total modal assurance criterion (MTMAC) and changes in natural frequencies are used to minimize by three optimization algorithms, viz, grey wolf optimizer (GWO), gradient-based optimization (GBO), and an improved version of GWO (IGWO). The influence of the S-RCFE and standard Euler-Bernoulli beam on the model updating accuracy is also examined, and the efficiency of S-RCFE is evaluated. The statistical results reveal that GWO-MTMAC and IGWO-MTMAC can be successfully implemented for FE model updating with almost the same performance. However, IGWO provides the most reliable results with a relatively extensive computation time for damage identification in all scenarios. In some damage scenarios, the GWO and GBO perform comparably with very similar running time. Data used in this article can be found at https://github.com/Samir-Khatir/Data-modal-analysis-of-healthy-and-cracked-beam.git |
Keywords | Damage identification; Semi-rigidly connected frame element ; Euler-Bernoulli beam element ; Grey wolf optimizer ; Gradient-based optimization ; Modified total modal assurance criterion ; Experimental beam |
ANZSRC Field of Research 2020 | 400510. Structural engineering |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Islamic Azad University, Iran |
Ho Chi Minh City Open University, Vietnam | |
Azarbaijan Shahid Madani University, Iran | |
School of Engineering | |
University of Science and Technology Oran, Algeria | |
Ghent University, Belgium |
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https://research.usq.edu.au/item/z2803/finite-element-model-updating-and-damage-identification-using-semi-rigidly-connected-frame-element-and-optimization-procedure-an-experimental-validation
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