Abstract
In the application of a bridge weigh-in-motion (WIM) system, the collected data may be temporarily or permanently lost due to sensor failure or system transmission failure. The high data loss rate weakens the distribution characteristics of the collected data and the ability of the monitoring system to conduct assessments on bridge condition. A deep learning-based model, or generative adversarial network (GAN), is proposed to reconstruct the missing data in the bridge WIM systems. The proposed GAN in this study can model the collected dataset and predict the missing data. Firstly, the data from stable measurements before the data loss are provided, and then the generator is trained to extract the retained features from the dataset and the data lost in the process are collected by using only the responses of the remaining functional sensors. The discriminator feeds back the recognition results to the generator in order to improve its reconstruction accuracy. In the model training, two loss functions, generation loss and confrontation loss, are used, and the general outline and potential distribution characteristics of the signal are well processed by the model. Finally, by applying the engineering data of the Hangzhou Jiangdong Bridge to the GAN model, this paper verifies the effectiveness of the proposed method. The results show that the final reconstructed dataset is in good agreement with the actual dataset in terms of total vehicle weight and axle weight. Furthermore, the approximate contour and potential distribution characteristics of the original dataset are reproduced. It is suggested that the proposed method can be used in real-life applications. This research can provide a promising method for the data reconstruction of bridge monitoring systems.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Zhejiang Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference46 articles.
1. Improving truck safety: Potential of weigh-in-motion technology
2. Fatigue reliability assessment of steel bridge details integrating weigh-in-motion data and probabilistic finite element analysis;Tong;Comput. Struct.,2012
3. Development of a Bridge Weigh-in-Motion System Based on Long-Gauge Fiber Bragg Grating Sensors
4. A review of computer vision–based structural health monitoring at local and global levels
5. A completely non-contact recognition system for bridge unit influence line using portable cameras and computer vision;Dong;Smart Struct. Syst.,2019
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