Vol.:(0123456789) 1 3 Innovative Infrastructure Solutions (2022) 7:247 https://doi.org/10.1007/s41062-022-00847-3 TECHNICAL PAPER Defect‑oriented supportive bridge inspection system featuring building information modeling and augmented reality Immanuel John Samuel 1  · Ossama Salem 1  · Song He 2 Received: 4 February 2022 / Accepted: 10 May 2022 © Springer Nature Switzerland AG 2022 Abstract Bridges are indispensable links of transportation infrastructure systems, and inspections play a critical role in maintaining bridge components in the state of good repair. Through a survey of bridge inspectors, the authors revealed that visual inspec- tion techniques are the prominent inspection method but result in inaccuracy and ambiguity due to high variances among inspection results; modern inspections using drones and robots could improve efciency but pose new challenges and do not reduce subjectivity. As a result, a novel, building information modeling- and augmented reality-based supportive inspection system (BASIS) that objectively captures bridge defects is proposed and validated. On-site inspectors can access the bridge model containing historical defect information (defect type, length/width/depth, and location) and overlay relevant content on the actual infrastructure through BASIS for inspection data collection with more accuracy and less ambiguity. A proof- of-concept prototype of the BASIS for bridges was developed as an android application and verifed by bridge inspectors for efectiveness on a small pedestrian bridge. It was found that BASIS was able to collect accurate inspection data irrespective of the level of experience of the user, thusly minimizing the data subjectivity caused by diferences among inspectors’ judg- ment and/or human errors. This research explores the utilization of emerging tools to collect bridge condition information in a more comprehensive and objective manner. Collected information can be further integrated it into a digital model that refects the bridge’s most accurate and up-to-date condition, heading toward a digital twin of the physical infrastructure. The proposed system may also be adapted for other types of infrastructure (e.g., dams, levees, and railroads) that also require routine inspections. Keywords Bridge inspections · Supportive systems · Defect information · Building information modeling · Augmented reality Introduction Visual inspections are the most frequently used method to inspect bridges. Inspectors identify the defects, examine their characteristics, assess the condition rating of the asset, and manually record their observations. In this process, inaccuracies and subjectivity are ubiquitous [1], caused by insufcient training and experience of personnel, unclear inspection requirements and procedures, improper inter- pretation of defects, and a lack of work ethic. To improve the efciency of the inspection process, modern/advanced technologies are used as supportive tools. However, this does not necessarily reduce subjectivity and has introduced new challenges such as high capital costs, complex operation/ handling, extensive training requirements, post-processing of condition data, and communication issues among difer- ent stakeholders [2, 3]. Many bridge owners are also reluc- tant to accept inspection data obtained by new technologies [4]. Therefore, it is important to support bridge inspectors with appropriate tools and technologies for better visual inspection results. Meanwhile, collecting accurate defect information with location details is expected to reduce the subjectivity of inspection data [5] and predict critical failure modes [6]. There is a consensus that building information models * Song He 20210465@wzu.edu.cn 1 Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA 2 Department of Civil Engineering, College of Civil Engineering and Architecture, Wenzhou University, Zhejiang 325035, China