Feature Selection and Deep Learning for Deterioration Prediction of the Bridges
Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 6
Abstract
Bridge deterioration is inevitable in service, and the inspection and maintenance of bridges are needed to ensure structural integrity. To make cost-effective inspection plans, bridge management departments need degradation models to predict future condition ratings of bridges. Although there have been studies on bridge degradation, the input features of models are selected mostly based on engineering experience, and no effective method has been proposed. Meanwhile, most models based on machine learning (ML) and deep learning (DL) only predict the degradation of bridges in a single year and cannot cover a complete inspection cycle (usually 2 years), providing limited decision support for the transportation departments. Besides, more accurate models are needed to predict the degradation trend of bridges. In response to these problems, an improved ReliefF algorithm is proposed to select features of bridges in the paper. Meanwhile, a new degradation model combining recurrent neural network (RNN) and convolutional neural network (CNN) is established. Historical data of bridges in the US state of Texas from 1992 to 2019 are employed to verify the proposed methods. The result shows that the improved ReliefF algorithm selects the appropriate feature set as the input of the prediction model, and the model accurately predicts the future condition ratings of bridges in the next 3–4 years. The research is beneficial to infrastructure management departments in allocating bridge inspection and maintenance resources reasonably.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The author would like to acknowledge members of the Key Laboratory of Coast Civil Structure Safety (Ministry of Education) and the research group of bridges at Tianjin University for their endless support. The paper uses the NBI data from the Federal Highway Administration (FHWA) of the US Department of Transportation. The authors greatly appreciate the engineering professionals of FHWA who collected bridge information. This work presented here was supported by the National Key R&D Program of China (2018YFB1600300 and 2018YFB1600301), the National Science Foundation of China (52078333), and the Tianjin Transportation Science and Technology Development Plan Project (G2018-29). Any opinions, findings, and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsor.
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© 2021 American Society of Civil Engineers.
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Received: Apr 19, 2021
Accepted: Jul 8, 2021
Published online: Aug 30, 2021
Published in print: Dec 1, 2021
Discussion open until: Jan 30, 2022
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