دورية أكاديمية
GRU neural network-based method for box girder crack damage detection
| العنوان: | GRU neural network-based method for box girder crack damage detection |
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| المؤلفون: | Xiedong LUO, Dongliang MA, Songlin ZHANG, Deyu WANG |
| المصدر: | Zhongguo Jianchuan Yanjiu, Vol 17, Iss 4, Pp 194-203 (2022) |
| بيانات النشر: | Editorial Office of Chinese Journal of Ship Research, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Naval architecture. Shipbuilding. Marine engineering |
| مصطلحات موضوعية: | gated recurrent unit (gru) neural network, box girder, crack detection, noise, Naval architecture. Shipbuilding. Marine engineering, VM1-989 |
| الوصف: | Objectives With the development of intelligent ships, it has been difficult for traditional crack damage detection methods to meet the detection requirements. This paper proposes a real-time crack damage detection method for box girders based on a gated recurrent unit (GRU) neural network. MethodsUsing the secondary development technology of Abaqus based on the Python language, a box girder crack damage model is built, and its acceleration response under dynamic Gaussian white noise excitation is calculated. A dataset is generated by expanding the original data using the data cropping method, and the influence of noise is considered. A box girder crack damage detection model based on GRU is established, the acceleration response dataset is directly used as input and the minimum loss function value is used as a target to train the model. This method is then compared to the wavelet packet transform-based multi-layer perceptron (WPT-MLP) model. ResultsThe comparison shows that the GRU model proposed in this paper has higher detection accuracy than the WPT-MLP model in damage location and extent detection. It is less sensitive to noise and has higher accuracy in approximate prediction. ConclusionsThe results of this study verify the applicability of GRU neural networks in the crack damage detection of box girders containing multiple plates. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English Chinese |
| تدمد: | 1673-3185 |
| العلاقة: | https://doaj.org/toc/1673-3185Test |
| DOI: | 10.19693/j.issn.1673-3185.02415 |
| الوصول الحر: | https://doaj.org/article/70f9a1168d6c42e39410f8529bb7b9f7Test |
| رقم الانضمام: | edsdoj.70f9a1168d6c42e39410f8529bb7b9f7 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 16733185 |
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| DOI: | 10.19693/j.issn.1673-3185.02415 |