基于深层感知器和计算机视觉技术的桥梁影响面识别

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Xudong Jian, Ye Xia, E. Chatzi, Zhilu Lai
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引用次数: 0

摘要

桥梁结构影响面的识别为理解交通荷载和评估结构状况提供了一种有效的工具。一般来说,真实桥梁的ISs可以通过使用已知重量的校准车辆在桥梁上行驶的校准测试来确定。然而,现有方法难以考虑标定车辆的横向运动、速度变化、轨道宽度等综合因素以及桥梁动力效应。这些因素不可避免地会给识别任务带来不准确性。为了综合考虑这些因素,本研究提出了一种基于深度学习的方法,该方法将深度多层感知器(MLP)与计算机视觉(CV)相结合,利用深度多层感知器识别桥梁ISs,利用CV获取标定车辆车轮位置坐标。通过一系列的数值模拟和在用桥梁的现场试验,验证了所提出的框架,并将其与广泛建立的方法进行了比较。结果表明,该框架具有较好的准确性、鲁棒性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques
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.
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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