利用干涉雷达系统对桥梁进行基于集合学习的结构健康监测

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Civil Structural Health Monitoring Pub Date : 2024-04-30 DOI:10.1007/s13349-024-00789-7
Ali Yaghoubzadehfard, Elisa Lumantarna, Nilupa Herath, Massoud Sofi, Mehmet Rad
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引用次数: 0

摘要

由于人口增加、城市化、交通发展以及现有桥梁的老化,对新型快速桥梁结构健康监测(SHM)的需求日益增长。为应对这一挑战,一种突出的方法是使用基于干涉雷达系统的设备,特别是用于结构的干涉测量频率图像(IBIS-FS)。IBIS-FS 以其便携性和非侵入式操作而著称,不需要与桥梁直接接触。本研究利用 IBIS-FS 捕获了一座人行天桥的固有频率和模态振型。获得的数据与有限元模型的结果一致,证明了 IBIS-FS 在捕捉模态参数方面的可靠性。在此基础上,该研究探索了基于集合的先进机器学习技术的应用。通过利用从 IBIS-FS 获取的数据,随机森林、梯度提升决策树 (GBDT) 和极端梯度提升 (XGBoost) 等算法被用于桥梁损伤检测。这些机器学习 (ML) 技术适用于分析 IBIS-FS 获取的桥梁不完整模态参数。研究重点是使用这些算法来解释模态参数的变化,特别是将损伤识别为元素刚度的降低。这种方法可以进行综合分析,将模态参数(包括模态振型和因噪声水平变化而改变的固有频率)作为输入输入到模型中。据观察,所有三种 ML 方法,尤其是随机森林方法,都能有效识别损坏的位置和严重程度,证明了训练过程的高效性。GBDT 和 XGBoost 在处理复杂数据集时的鲁棒性也为其在桥梁损坏检测中的应用提供了巨大的前景。总之,这些结果凸显了将随机森林、GBDT 和 XGBoost 等先进的 ML 技术与从 IBIS-FS 获取的数据相结合的潜力。
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Ensemble learning-based structural health monitoring of a bridge using an interferometric radar system

Due to the increase in population, urbanisation, transportation development, and the aging of existing bridges, there is a growing need for new and rapid structural health monitoring (SHM) of bridges. To address this challenge, a method that stands out is the use of an interferometric radar system-based device, specifically Image by Interferometric Survey-Frequency for structures (IBIS-FS). Known for its portability and non-intrusive operation, IBIS-FS does not require direct contact with the bridge. This study utilised IBIS-FS to capture a pedestrian bridge’s natural frequencies and mode shapes. The data obtained were found to be consistent with results from finite element models, demonstrating the reliability of IBIS-FS in capturing modal parameters. Building upon this foundation, the study then explores the application of advanced ensemble-based machine-learning techniques. By leveraging the data acquired from IBIS-FS, algorithms such as Random Forest, Gradient-boosted Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost) are used for bridge damage detection. These machine-learning (ML) techniques are suited to analyse the incomplete modal parameters of bridges, as captured by IBIS-FS. The study focuses on using these algorithms to interpret the changes in modal parameters, specifically identifying damage as a reduction in the stiffness of elements. This approach allows for a comprehensive analysis, where the modal parameters, including mode shapes and natural frequencies altered by varying noise levels, are fed as input to the models. It was observed that all three ML methods, with Random Forest in particular, can effectively identify the location and severity of damage, demonstrating an efficient training process. The robustness of GBDT and XGBoost in handling complex data sets also shows great promise for their application in bridge damage detection. Collectively, these results underscore the potential of combining advanced ML techniques like Random Forest, GBDT, and XGBoost with the data acquired from IBIS-FS.

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来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
期刊最新文献
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