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Steel and Composite Structures Volume 46, Number 6, March25 2023 , pages 731-744 DOI: https://doi.org/10.12989/scs.2023.46.6.731 |
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A novel method for vehicle load detection in cable-stayed bridge using graph neural network |
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Van-Thanh Pham, Hye-Sook Son, Cheol-Ho Kim, Yun Jang and Seung-Eock Kim
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| Abstract | ||
| Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system. | ||
| Key Words | ||
| cable-stayed bridge; deep learning; graph neural network; practical advanced analysis; structural health monitoring; vehicle load detection | ||
| Address | ||
| Van-Thanh Pham, Cheol-Ho Kim, Yun Jang and Seung-Eock Kim:Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea Hye-Sook Son:Department of Computer Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea | ||