A review of mobile sensing of bridges using moving vehicles: Progress to date, challenges and future trends. (October 2022)

• Record Type: Journal Article
• Authors: Malekjafarian, Abdollah; Corbally, Robert; Gong, Wenjie
• Is Part Of: Structures. Volume 44(2022)
• Page Start: 1466
• Page End: 1489
• Publication Date: 2022-10
• Subjects: Mobile sensing, drive-by -- Indirect -- Bridge -- SHM -- Condition assessment; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2022.08.075
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Sensors installed on moving vehicles have been extensively used for condition monitoring and structural assessment of bridges in the recent years. These methods which are known as mobile sensing, indirect or drive-by bridge monitoring rely on the fact that the dynamic responses measured on a passing vehicle on a bridge contain bridge-related components. Therefore, they can be used for the identification of dynamic characteristics of the bridge and also assessing its structural condition. The critical advantage of these methods is that several bridges in a transport network can be monitored using one or multiple instrumented vehicles which play the role of moving sensors. This would result in a relatively cheaper bridge health monitoring system at the scale of an entire transport network. This paper provides an extensive review of the recently published works on mobile sensing for bridge condition monitoring. It presents the theoretical basis of vehicle bridge interaction where the main components of the vehicle response that can be used for bridge monitoring are fundamentally discussed. The drive-by bridge monitoring approaches are categorised into two main groups of the methods are proposed for (a) bridge modal identification and (b) bridge condition monitoring. The most recent approaches proposed in each category are reviewed based on numerical and experimental studies. In addition, the recent trends in bridge mobile sensing towards crowd sensing and the use of machine learning are discussed. The important role of experimental validation and field-testing is highlighted and existing laboratory scale models are compared. Furthermore, the paper provides a critical discussion around the main challenges to be overcome and future research direction for these methods to be successfully implemented in practice.