Enhanced image processing and storage for defect evaluation in BIM of inspected steel bridges

Every structure undergoes a maintenance lifecycle, with increasing emphasis on structural health monitoring, defect diagnosis, and repair. As the demand for timely, effective, practical, and cost-efficient solutions grows, new techniques and processes are continually being developed. The traditionally paper-based methods of bridge inspection, defect diagnosis, and repair are evolving into digital processes. The overall efficiency targeted by this digital transformation process can be increased by 35–50 percent when criteria such as time, cost, accuracy and applicability are evaluated. This paper presents an automated data collection and analysis procedure related to structural integrity evaluation. The study focuses on optimizing human-operated inspection procedures by incorporating advanced technologies, such as image processing techniques for defect identification in a digital environment, and integrating defect information into Building Information Modeling (BIM) systems. These focal points and processes, aiming to identify defect identification, and defect information management in BIM environment, are further explored and demonstrated through a case study involving a steel bridge. At the beginning of the study, approximately 160 defect images were collected, examined and some of them were selected for visualization purposes. The authors table a procedure that can be followed, starting with storing defect images and ending with defect visualization in the BIM environment, and present a discussion on the advantages and limits of the process by examining concepts such as automated data collection by drones, machine vision-based damage identification by color detection technique, BIM and Industry Foundation Classes standardisation applied to inspection data.