PSI - Issue 64

Kun Feng et al. / Procedia Structural Integrity 64 (2024) 596–603

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Kun Feng et al. / Structural Integrity Procedia 00 (2019) 000 – 000

1. Introduction The UK government is dedicated to reaching net zero carbon emissions by 2050. However, given the transport sector's significant contribution to pollution, this net zero goal cannot be realized without adequately tackling the emissions generated by transportation. According to the latest study by Lydon et al. (Lydon, Lydon et al. 2021), comprehending the obstacles to achieving net zero in transportation is crucial. Generally, there are two different approaches to decarbonizing transport through (1) reducing fleet emissions, and (2) enhancing the resilience of infrastructure. From a civil engineering perspective, Structural Health Monitoring (SHM) stands out as an effective method for implementing the latter approach: infrastructure resilience enhancement. The SHM technique is widely utilized to evaluate the reliability or remaining fatigue life of transportation infrastructure, employing advanced sensing technologies. Vibration-based SHM can be divided into two distinct methods based on sensor placement: the direct method and the indirect method. The direct method has undergone extensive practical research in recent decades (Huang, Yin et al. 2022), (Zhang, Wan et al. 2023), (Huang, Yin et al. 2024), due to its ability to deliver highly accurate SHM data, with sensors directly attached to the structures of interest. However, this method often necessitates a significant number of onsite sensors and additional data acquisition and transmission equipment, making it expensive. Moreover, this fixed sensing system cannot be rapidly deployed across extensive transport infrastructures. Consequently, in 2004, Yang et al. introduced the indirect method, which theoretically derives bridge frequencies from the dynamic response of a passing vehicle (Yang, Lin et al. 2004). Furthermore, the practical applications of the indirect method have been further explored, both numerically (Yang and Lin 2005) and through experimental research (Lin and Yang 2005). Latterly, the indirect method is widely known as drive-by monitoring (Kim, Kawatani et al. 2008), or vehicle scanning method (Yang, Yang et al. 2019). Since its initial development in 2004, drive-by monitoring has become a prevalent method for evaluating ageing infrastructure globally, encompassing theoretical research (McGetrick, González et al. 2009), (Yang, Li et al. 2012), numerical simulations (Kildashti, Alamdari et al. 2020), (Li, Lan et al. 2023), and experimental validations (Li, Lin et al. 2023), (Lan, Li et al. 2023). Moreover, in 2019, the European Union Joint Research Centre highlighted drive-by monitoring (e.g., vehicle-based SHM approach) as one of the most promising techniques for bridge SHM (Gkoumas, Marques Dos Santos et al. 2019), together with camera-based, drone-based, and satellite-based SHM approaches. Recently, there has been a growing interest in drive-by fleet monitoring, a technique where bridge information is derived from data collected by a fleet of vehicles. This approach, which contrasts with traditional drive-by monitoring that relies on a single vehicle crossing, offers a more reliable evaluation of structural reliability by reducing the uncertainties associated with individual vehicles. For instance, OBrien et al. introduced a novel bridge damage indicator called the moving reference influence function, designed to identify bridge bearing damage through deflections calculated from vehicle accelerations, presumed to be gathered from a partially equipped vehicle fleet. The findings indicate that both the area and skewness of the proposed damage indicator respond sensitively to damage, enabling the localization and quantification of the bridge ’s bearing damage (OBrien, McCrum et al. 2023), (McCrum, Wang et al. 2023), and the proposed approach was proved to be successful for bearing damage detection even without knowing individual vehicle properties (OBrien, McCrum et al. 2024). It is important to emphasize that the precise estimation of the deflections from the vehicle accelerations (or, the apparent profile (OBrien and Keenahan 2015), the contact point displacement (Zhang, Qian et al. 2018)) is crucial for identifying the bridge ’s bearing damage. Furthermore, another challenge arises from the variability in the properties of the vehicle fleet, which complicates the acquisition of precise bridge information from drive-by measurements. This study delves into the potential of employing a bus network-based drive-by fleet monitoring system for the structural health monitoring of bridges. It entails collecting vehicle dynamics measurements from a fleet comprising thousands of buses. Given the similarities in vehicle properties among the majority of these buses, this bus network based approach is anticipated to offer enhanced advantages in extracting bridge information from the drive-by measurements. In this investigation, the Operating Deflection Shapes (ODS) (Corbally and Malekjafarian 2024) are calculated from the drive-by contact point accelerations. The difference or the squared errors between the ODS curves of a healthy bridge and those of a damaged one serve as indicators of the bridge's structural integrity and can help in localizing bridge damage. The preliminary investigations are validated through numerical experiments.