Deep Learning Models for Time-History Prediction of Vehicle-Induced Bridge Responses: A Comparative Study

Author:

Li Huile12,Wang Tianyu12,Yang Judy P.3,Wu Gang12

Affiliation:

1. Key Laboratory of Concrete and Prestressed Concrete, Structures of the Ministry of Education, School of Civil Engineering, Southeast University, Nanjing, China

2. National and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing, China

3. Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Abstract

Time-history responses of the bridge induced by the moving vehicle provide crucial information for bridge design, operation, maintenance, etc. As inspired by this, this work attempts to provide a new paradigm for vehicle–bridge interaction (VBI) by highlighting the comparison of different deep learning algorithms applied to the prediction of time-history responses of the bridge under vehicular loads. Particularly, three deep learning architectures with few and measurable input features developed by using fully-connected feedforward neural network, long short-term memory (LSTM) network, and convolutional neural network (CNN) are proposed on the basis of the governing equation of bridge vibrations. Three VBI systems with various vehicle models are developed and further validated to produce reliable training data. To examine the accuracy of the predictive models, two advanced metrics are exploited for time-history estimate. Moreover, the proposed deep learning models are comprehensively investigated through a parametric study on the influential factors associated with the VBI system and network architecture. The results show that deep feedforward neural network (DFNN), LSTM network, and CNN can be applied in VBI analysis to estimate the bridge time-history response. The three neural networks have comparable prediction accuracies. When considering the irregularity excitation, CNN is found to be the most efficient predictive model, while DFNN needs the least training time under perfect bridge surface condition.

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Ltd

Subject

Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Building and Construction,Civil and Structural Engineering

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