Review of development on convolution neural network based structural health monitoring on bridges

Abstract

Structural Health Monitoring (SHM) has been a rising interest in civil, mechanical and aerospace research. Bridge health monitoring system is able to issue early warning on damages as well as appraising the durability and reliability on bridges. Bridges are exposed to several damages such as wind, overloading and earthquakes, to ensure its structural integrity and avoid costly repairs on late stage deterioration, utilizing Machine Learning (ML) algorithms in SHM on complex structures becomes more essential to bridge SHM systems in order to assess structural damage. With the increasing number of studies on integrating convolutional neural network (CNN) in SHM on the bridge. This paper aims to provide a review on the development of CNN based SHM on bridges with a case study.