Damage Identification of Conduit Rack in Offshore Platform Structures Based on a Novel Composite Neural Network
Structural health monitoring (SHM) of offshore jacket platforms is crucial, and currently traditional deep learning methods such as artificial neural networks (ANNs) are widely used in damage identification of offshore conduit rack platform structures, which focusses on mapping feature information c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2025-04-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | https://eejournal.ktu.lt/index.php/elt/article/view/40795 |
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| Summary: | Structural health monitoring (SHM) of offshore jacket platforms is crucial, and currently traditional deep learning methods such as artificial neural networks (ANNs) are widely used in damage identification of offshore conduit rack platform structures, which focusses on mapping feature information caused by damage to structural damage patterns. However, traditional methods have limitations in dealing with the time series data in the feature information. To improve the application of the time series information generated from offshore platform structures in damage modes, we propose a new integrated deep learning network model, which is used to improve the accuracy of the damage mode recognition based on the acceleration information of the conduit rack structure. First, the temporal convolutional network (TCN) breaks through the localisation of traditional convolutional neural networks in modelling the temporal dimension by efficiently extracting the long-term time since of the structural vibration response through an expansive causal convolution mechanism. Second, the bidirectional long short-term memory network (BiLSTM) further extracts the contextual information and global features of the data by extracting feature information in both directions and fusing the before and after correlations of vibration response signals. In addition, we adopt the Newton-Raphson-based optimiser (NRBO) optimisation algorithm for global optimisation of the hyperparameters of TCN and BiLSTM to avoid the subjectivity of manual parameter tuning, which significantly improves the model convergence speed and generalisation performance. Experimentally validated by finite element model simulation and testbed construction, our proposed NRBO-TCN-BiLSTM combined neural network damage identification accuracy is as high as 99 % on average, exceeding existing deep learning methods. The method has a wide range of applications in SHM for offshore platforms. |
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| ISSN: | 1392-1215 2029-5731 |