Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques

Abstract

The identification of influence surfaces (ISs) for bridge structures offers an efficient tool for understanding traffic loads and assessing structural conditions. In general, ISs of a real bridge can be identified through calibration tests using calibration vehicles with known weights moving across the bridge. However, the existing methods face difficulties in considering comprehensive factors, such as the lateral movement, speed variation, and track width of the calibration vehicle, as well as bridge dynamic effects. These factors inevitably introduce inaccuracies into the task of identification. To comprehensively consider these factors, this study proposes a deep learning-based method that combines deep multilayer perceptrons (MLPs) and computer vision (CV), with deep MLP adopted to identify bridge ISs and CV employed to obtain the position coordinates of the calibration vehicle’s wheels. A series of numerical simulations and field experiments on an in-service bridge were carried out to validate the proposed framework and compare it against a broadly established method to such an end—Quilligan’s method. The results show the accuracy, robustness, and practicability of the proposed framework.

Vehicle Load Identification Using Machine Vision and Displacement Influence Lines

“…With the assistance of computer vision technology, the spatiotemporal distribution of vehicles can be obtained. To improve the application effectiveness under complex driving conditions, establishing the structural response influence surface is one effective solution [23,24]. It should be noted, however, that the research objects in this paper have only one lane in each driving direction, and the lateral loading effects are not significant.…”