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Steel and Composite Structures Volume 44, Number 2, July 2022 , pages 241-254 DOI: https://doi.org/10.12989/scs.2022.44.2.241 |
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Cable damage identification of cable-stayed bridge using multi-layer perceptron and graph neural network |
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Van-Thanh Pham, Yun Jang, Jong-Woong Park, Dong-Joo Kim and Seung-Eock Kim
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| Abstract | ||
| The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data. | ||
| Key Words | ||
| cable-stayed bridge; cable damage identification; deep learning; graph neural network; multi-layer perceptron; vibration characteristics | ||
| Address | ||
| Van-Thanh Pham:Department. of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea Yun Jang:Department. of Computer Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea Jong-Woong Park:School of Civil and Environmental Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, South Korea Dong-Joo Kim:Department. of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea Seung-Eock Kim:Department. of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea | ||