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Structural Engineering and Mechanics Volume 70, Number 4, May25 2019 , pages 457-466 DOI: https://doi.org/10.12989/sem.2019.70.4.457 |
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Hierarchical neural network for damage detection using modal parameters |
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Minwoo Chang, Jae Kwan Kim and Joonhyeok Lee
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| Abstract | ||
| This study develops a damage detection method based on neural networks. The performance of the method is numerically and experimentally verified using a three-story shear building model. The framework is mainly composed of two hierarchical stages to identify damage location and extent using artificial neural network (ANN). The normalized damage signature index, that is a normalized ratio of the changes in the natural frequency and mode shape caused by the damage, is used to identify the damage location. The modal parameters extracted from the numerically developed structure for multiple damage scenarios are used to train the ANN. The positive alarm from the first stage of damage detection activates the second stage of ANN to assess the damage extent. The difference in mode shape vectors between the intact and damaged structures is used to determine the extent of the related damage. The entire procedure is verified using laboratory experiments. The damage is artificially modeled by replacing the column element with a narrow section, and a stochastic subspace identification method is used to identify the modal parameters. The results verify that the proposed method can accurately detect the damage location and extent. | ||
| Key Words | ||
| artificial neural network; damage detection; normalized damage significance index; modal identification; structural health monitoring | ||
| Address | ||
| Minwoo Chang: Northern Railroad Research Center, Korea Railroad Research Institute, 176 Cheoldo bangmulgwan-ro, Uiwang-si, Gyeonggi-do 16105, Republic of Korea Jae Kwan Kim: Department of Civil and Environmental Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea Joonhyeok Lee: Infrastructure ENG Team, Samsung C&T Corporation, 26 Sangil-ro 6-gil, Gangdong-gu, Seoul 05288, Republic of Korea | ||