Cronin Liam, Sen Debarshi, Marasco Giulia, Dabbaghchian Iman, Benedetti Lorenzo, Matarazzo Thomas, Pakzad Shamim
Department of Civil and Environmental Engineering, Lehigh University, 117 ATLSS Dr., Bethlehem, PA 18015, USA.
Department of Civil Engineering, Southern Illinois University Carbondale, 1263 Lincoln Dr., Carbondale, IL 62901, USA.
Sensors (Basel). 2025 Apr 17;25(8):2528. doi: 10.3390/s25082528.
Vibration-based bridge modal identification is a crucial tool in monitoring and managing transportation infrastructure. Traditionally, this entails deploying a fixed array of sensors to measure bridge responses such as accelerations, determine dynamic characteristics, and subsequently infer bridge conditions that will facilitate prognosis and decision-making. However, such a paradigm is not scalable, possesses limited spatial resolution, and typically entails high effort and cost. Recently, mobile sensing-based paradigms have demonstrated promise in laboratory and field settings as an alternative. These methods can leverage big data from crowdsourcing vibration data acquired from smartphone devices belonging to pedestrians and passengers traveling over a bridge, constituting a significantly large data stream of indirectly sensed bridge response. Although the efficacy of such a paradigm has been demonstrated for a limited set of case studies, ubiquitous implementation requires analyzing the impact of vehicle dynamics and quantifying data sources that can be used for the purpose of bridge modal identification. This paper presents a road map for achieving this through dynamically diverse datastreams such as passenger cars, buses, bikes, and scooters. Existing datastreams point towards the implementation of crowdsourced mobile sensing paradigms in urban settings, which would facilitate effective decision-making for enhanced transportation infrastructure resilience.
基于振动的桥梁模态识别是监测和管理交通基础设施的关键工具。传统上，这需要部署固定的传感器阵列来测量桥梁响应，如加速度，确定动态特性，随后推断有助于预后和决策的桥梁状况。然而，这种模式不可扩展，空间分辨率有限，而且通常需要付出高昂的努力和成本。最近，基于移动传感的模式在实验室和现场环境中作为一种替代方案已展现出前景。这些方法可以利用从在桥上行驶的行人和乘客的智能手机设备获取的众包振动数据中的大数据，构成间接感知的桥梁响应的大量数据流。尽管这种模式的有效性已在有限的一组案例研究中得到证明，但普遍实施需要分析车辆动力学的影响并量化可用于桥梁模态识别目的的数据源。本文提出了一个路线图，通过客车、公交车、自行车和踏板车等动态多样的数据流来实现这一目标。现有数据流指向在城市环境中实施众包移动传感模式，这将有助于为增强交通基础设施弹性做出有效的决策。
