Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM of short‐ to medium‐size bridges requiring no sensors to be installed on the structure. However, drive‐by SHM is generally known as a short‐term monitoring technique due to the challenges associated with using multiple passages of instrumented vehicles for a long time. This paper proposes combining the potentiality of connected and autonomous vehicles (CAVs) into drive‐by damage detection by introducing In‐Fleet SHM. To the authors’ knowledge, this is the first study that proposes using CAVs for SHM application in civil engineering structures. Each In‐Fleet CAV could automatically collect the vehicle's persistent and temporal data by the embedded sensors and transmit them to edge computing systems for analysis. These persistent data include type and model and temporal parameters encompassing position, speed, heading, and vertical acceleration of CAVs. Knowing the persistent and temporal data of the passing vehicles over the transportation infrastructures enables the identification of the dynamic parameters of the bridge from the vehicles’ vertical acceleration response using drive‐by techniques and, on the other hand, reconstruction of the finite element model of the passing vehicles over the supporting bridges in a near real‐time manner. In contrast to the drive‐by SHM, In‐Fleet monitoring has an expanded spatial and temporal coverage, enabling continuous near real‐time monitoring of highway bridges of the transportation network. The accuracy and resolution of the identified modal components in In‐Fleet SHM are enhanced due to the crowdsensing nature of the collected data. Furthermore, by offering a unique set of characteristics, this method fills the crucial gap in implementing Industry 4.0 technologies and digital twins for SHM of bridges.

中文翻译：

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使用联网和自动驾驶车辆的数据对道路桥梁进行车队结构健康监测

驾驶式结构健康监测 (SHM) 是中短型桥梁直接 SHM 的一种经济高效的替代方案，无需在结构上安装传感器。然而，由于长时间使用仪表车辆的多个通道带来的挑战，驾驶式健康监测通常被认为是一种短期监测技术。本文建议通过引入 In-Fleet SHM 将联网自动驾驶车辆 (CAV) 的潜力结合到路过损坏检测中。据作者所知，这是第一项建议在土木工程结构中使用 CAV 进行 SHM 应用的研究。每个车队内的 CAV 都可以通过嵌入式传感器自动收集车辆的持久数据和临时数据，并将其传输到边缘计算系统进行分析。这些持久数据包括 CAV 的类型、型号和时间参数，包括位置、速度、航向和垂直加速度。了解交通基础设施上过往车辆的持续和时间数据，可以使用驾驶技术从车辆的垂直加速度响应中识别桥梁的动态参数，另一方面，重建桥梁的有限元模型以近乎实时的方式显示支撑桥上的过往车辆。与驾车式 SHM 相比，车队内监控具有扩展的空间和时间覆盖范围，能够对交通网络的公路桥梁进行连续的近实时监控。由于所收集数据的群体感知性质，In-Fleet SHM 中识别的模态分量的准确性和分辨率得到了提高。此外，通过提供一组独特的特性，该方法填补了实施工业 4.0 技术和桥梁 SHM 数字孪生的关键空白。