A human-centered infrastructure asset management framework using BIM and augmented reality

To maintain and preserve bridges at an acceptable level of service, it is necessary to conduct a variety of inspections at varying frequencies. Bridges are vital components of the United States' infrastructure system. Traditional physical and visual inspections are subjective and time-consuming, and they are unable to meet the objectives of element-level asset management, which is becoming increasingly important. According to FHWA guidelines, advanced inspection methods such as Unmanned Aerial Vehicles (UAVs), Computer Vision, Artificial Intelligence, sensors and robots can only be used as supporting tools, and they introduce new challenges to the inspection process, such as complex operation/handling, cost of new devices including operation and maintenance, post-processing of condition data, and communication issues between different stakeholders. This study proposes a Building Information Modeling (BIM) and Augmented Reality (AR)-based supporting inspection system (BASIS) that can record bridge defect information objectively. A pedestrian bridge is used to validate the inspection platform. For asset management decisions, accurate condition evaluation of in-service infrastructure systems is essential. Most of the time, subjective indices are used to describe visual observations when judging the structural condition of highway bridges. This research proposes using finite element analysis to comprehensively evaluate the element-level condition of infrastructure assets. Using synthetic data of a bridge, the condition assessment system is validated. The Assessment is followed by Prioritization, which is accomplished with the Analytical Hierarchy Process (AHP), which defines the relative significance of various asset criteria. Current decision making is not centered on maintenance priorities at the element level. This study's innovative method for evaluating the condition of assets at the element level is a key step toward achieving more objective and comprehensive evaluation methodologies. Making the inspection process less expensive, less intrusive, more quantitative, and more consistent is therefore an important area of research. Particularly, there is a need for improved techniques of documenting the visual representation and precise position of faults at element level in an asset at a certain inspection interval, so as to generate inspection data that is more objective and consistent. This dissertation provides a computational system that merges Building Information Modeling (BIM) with Augmented Reality (AR) to deliver precise defect information of assets, thereby decreasing costs, accelerating inspection, and facilitating quantitative condition evaluation. The asset management system then examines and prioritizes assets at the element level in order to increase the long-term performance of bridges. The precision, efficiency, flexibility, and practicability of the provided framework were tested 3 by comparing its performance on major infrastructure systems to that of conventional methods.