Digital Twins for Visual Inspection of Large Structures: A Systematic Review

Abstract

There is an increasing demand for reliable and automated visual inspection methods for large civil and industrial structures such as bridges, dams and transportation infrastructure. Traditional visual inspection methods for large structures are struggling with accessibility, scale, and data consistency. Digital twins (DTs) are transforming structural health monitoring. DTs address these limitations with the help of drone or robotic imagery, integration of multi-modal data and repeatable inspections, giving inspectors a virtual walk-through of the structure and its condition. We examine the state of the art in visual inspection DTs that detect and predict surface damage in large structures. We study the maturity levels of 112 DT deployments from perspectives of Business, Usage, Functional and Implementation Viewpoints. The DTs are further classified into five categories: Supervisory, Operational, Simulation, Intelligent and Autonomous, based on the levels of their implementation across the full DT pipeline of data acquisition, data exchange, DT model creation, visualisation and use of Artificial Intelligence (AI). This paper also highlights various design considerations these implementations take into account to overcome challenges observed in earlier visual inspection DTs. It also examines the extent to which AI is integrated into these implementations and the capability of these DTs to provide predictive insights and maintenance planning. The paper ends with a brief description of key findings and research gaps.