A review of recent research on visual inspection processes for bridges and the potential uses of AI

Abstract

Visual inspection remains an essential tool for assessing structural damage. Damage detection is a challenging task for those specifying, designing, and deploying SHM systems. Often only traditional visual inspection processes are available to determine the type and extent of structural damage. For bridge structures in the UK, a regime of general (every two years) and principal (every six years) inspections is often followed. Such visual inspections are time-consuming and costly in terms of both labour and financial resources. Therefore, the possibility of completing more of the bridge visual inspection process offsite has many potential benefits for bridge owners and managers during the service life of the asset. Recent research conducted at the University of Bristol in collaboration with industrial partners has examined how to make the best use of metrics derived from visual inspection data when assessing bridge condition and planning maintenance activities. Recent research into which aspects of the current visual inspection regime in the UK could potentially be moved offsite has also been carried out. This paper summarises these research efforts and discusses how AI may be used as part of future enhancements to visual inspection data capture and analysis.