A Review of Latest Trends in Bridge Health Monitoring

Abstract

Structural damage is inherent in civil engineering structures and bridges are no exception. It is vital to monitor and keep track of damage on bridge structures due to multiple mechanical, environmental, and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge Health Monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity, and serviceability of bridges; maintaining the uninterrupted operation of a bridge highly relies on understanding the development and propagation of damage. BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade.  Emerging methods, as the products of the technology advancements, resulted in handy tools that have been quickly adopted by bridge engineers. State-of-the-art techniques such as LiDAR, Photogrammetry, Virtual Reality (VR) and Augmented Reality (AR), Digital Twins, Computer Vision, Machine Learning, and Deep Learning are now integrated part of the new-generation BHM operations. This paper presents a brief overview of these latest BHM technologies.