Deep Learning Overloaded Vehicle Identification for Long Span Bridges Based on Structural Health Monitoring Data

TL;DR

This paper introduces a deep learning method for identifying overloaded vehicles on long-span bridges using structural health monitoring data, eliminating the need for prior vehicle information and handling multiple vehicles effectively.

Contribution

A novel deep learning model utilizing temporal convolutional networks for overloaded vehicle identification from SHM data, without requiring influence lines or vehicle parameters.

Findings

Outperforms traditional machine learning methods in accuracy.

Demonstrates robustness under random traffic flow conditions.

Effective on both simple and complex bridge structures.

Abstract

Overloaded vehicles bring great harm to transportation infrastructures. BWIM (bridge weigh-in-motion) method for overloaded vehicle identification is getting more popular because it can be implemented without interruption to the traffic. However, its application is still limited because its effectiveness largely depends on professional knowledge and extra information, and is susceptible to occurrence of multiple vehicles. In this paper, a deep learning based overloaded vehicle identification approach (DOVI) is proposed, with the purpose of overloaded vehicle identification for long-span bridges by the use of structural health monitoring data. The proposed DOVI model uses temporal convolutional architectures to extract the spatial and temporal features of the input sequence data, thus provides an end-to-end overloaded vehicle identification solution which neither needs the influence line nor needs to obtain velocity and wheelbase information in advance and can be applied under the occurrence of multiple vehicles. Model evaluations are conducted on a simply supported beam and a long-span cable-stayed bridge under random traffic flow. Results demonstrate that the proposed deep-learning overloaded vehicle identification approach has better effectiveness and robustness, compared with other machine learning and deep learning approaches.