通过众包智能手机数据的压缩传感技术检测桥梁结构的损坏情况

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Mohammad Talebi-Kalaleh, Qipei Mei
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引用次数: 0

摘要

传统的桥梁健康监测方法需要安装传感器,不仅成本高昂,而且耗费时间。相比之下,利用智能手机收集的车辆通过桥梁时的数据,则是一种高效且经济的替代方法。本文介绍了一种用于间接监测桥梁结构的尖端损坏检测框架,该框架利用了从经过桥梁的车辆上的智能手机收集到的大量加速度数据。我们的创新方法通过将压缩传感(CS)集成到基于群体传感的监测框架中,解决了在收集和传输高频数据的同时保留智能手机电池寿命和数据计划的难题。与奈奎斯特-香农采样定理的要求相比,CS 采用随机采样,并从大幅减少的采样数量中恢复信号。在建议的框架中,车辆的加速度信号最初是通过智能手机传感器获取的,经过压缩后再传输进行信号重建。随后,利用梅尔频率共振频率系数和主成分分析进行特征提取和降维。根据概率分布函数之间的不相似性,利用 Wasserstein 距离度量计算损坏指数。通过利用数值模型和实验室规模的桥梁,证明了所提出的方法在桥梁监测中的有效性。此外,还利用金门大桥上 102 次车辆行驶的智能手机数据,研究了在实际应用中实施该框架的可行性。结果表明,使用通过压缩传感获得的重建信号进行损坏检测,其性能与以奈奎斯特测量采样率采样的原始数据相当。但是,为了保留信号中的严重程度信息,以便准确识别损坏严重程度,压缩水平应限制在 20%。这些研究结果证实,压缩传感大大降低了基于群体传感的监测应用对数据收集的要求,同时又不影响损坏检测的准确性,并保留了数据集中对损坏敏感的基本信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

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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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