Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract

Railway bridges are the vital element of the railway infrastructures, whose safety directly affects the regional economy and commuter transportation. However, railway bridges are often subjected severe loading and working conditions, caused by traffic growth and heavier vehicles, and the increase in train running speed further makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the safety and reliability of the overall railway bridges is the bridge structural health monitoring (SHM), which not only monitors the structural conditions of bridges and maintains train operation safety, but also helps to expand the lifespan of bridges by enhancing the durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provided a review or overview on SHM of railway bridges. Some of the review papers have become obsolete and are not reflective of the state-of-the-art research. Therefore, the main goal of this article is to summarize the state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. the fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Then the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, the issues related to the future directions and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.