Multidamage Detection of Breathing Cracks in Plate-Like Bridges: Experimental and Numerical Study

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-10-29 DOI:10.1155/2024/8840611
Cheng Wang, Kang Gao, Zhen Yang, Jinlong Liu, Gang Wu
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Abstract

Bridges may develop breathing cracks under excessive overloading vehicles, while conventional beam models are ineffective in analyzing the effect of spatial distribution of these cracks. This study proposes a data-driven detection model with the consideration of spatial distribution of breathing cracks that can detect the multiple damage locations and degrees of breathing cracks in plate-like bridges. Firstly, a 2D vehicle–bridge interaction model containing breathing cracks is established, and the damage indicator, contact point displacement variation (CPDV), is calculated using vehicle acceleration data. Next, a dataset with CPDV as the input feature is generated using the finite element method to train the CatBoost-based damage prediction model, which considers the random distribution of single and multiple cracks, as well as the influence of different vehicle speeds. Finally, by calculating the CPDV related to the actual bridge and feeding it into the trained model, the location and degree of the damage can be predicted. The numerical simulation results demonstrate that this approach can accurately detect complex crack information under various vehicle speeds and exhibits robustness against road roughness. A laboratory experiment further confirms the effectiveness, applicability, and feasibility of this method to multiple damage locations and degree of breathing cracks.

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板状桥梁呼吸裂缝的多损伤检测:实验与数值研究
在超载车辆的作用下,桥梁可能会出现呼吸裂缝,而传统的梁模型无法有效分析这些裂缝的空间分布影响。本研究提出了一种考虑呼吸裂缝空间分布的数据驱动检测模型,可检测板状桥梁呼吸裂缝的多个损伤位置和损伤程度。首先,建立了包含呼吸裂缝的二维车桥相互作用模型,并利用车辆加速度数据计算了损伤指标--接触点位移变化(CPDV)。然后,使用有限元法生成以 CPDV 为输入特征的数据集,训练基于 CatBoost 的损伤预测模型,该模型考虑了单裂缝和多裂缝的随机分布以及不同车速的影响。最后,通过计算与实际桥梁相关的 CPDV 并将其输入训练好的模型,就可以预测损坏的位置和程度。数值模拟结果表明,这种方法可以在不同车速下准确检测复杂的裂缝信息,并对路面粗糙度表现出鲁棒性。实验室实验进一步证实了这种方法对多种损坏位置和呼吸裂缝程度的有效性、适用性和可行性。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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