Date of Award


Degree Type


Degree Name

Master of Science in Civil and Environmental Engineering


Civil and Environmental Engineering

First Advisor

Mayrai Gindy


The bridge infrastructure in the United States, and particularly in Rhode Island, has deteriorated over the last decades. The state of Rhode Island is placed last in the United States’ bridge condition ranking. To counteract the steady deterioration, it is necessary to have an overview of the current bridge conditions by implementing a Bridge Management System. Bridge inspections are the first entity in an effective Bridge Management System since they assess the bridge condition on site.

This thesis investigates two technologies that are promising to enhance and digitize the bridge inspection processes. Augmented Reality (AR) and Building Information Modeling (BIM) are techniques that have gained interest in the architecture and construction industries in the last decade.

Before analyzing the state-of-the-art bridge inspection processes, first a comprehensive literature review about the current bridge inspection methods and condition rating in the United States is conducted. Then, the two technologies, AR, and BIM, are exemplified and analyzed regarding their feasibility for bridge inspection purposes.

Next, a Bridge Information Data Model (BIDM) is developed. It is a 3D-database for storing and accessing inspection data on an element level, which follows BIM principles. Bridge elements can be addressed separately allowing the review of inspection history and the linkage of new defects.

Testing the applicability of the developed BIDM, a case study is conducted. It is found that the main capabilities of the BIDM are the enhanced comprehension of the bridge structure, since it displays the bridge as a 3D digital twin, the enhanced traceability of location and inspection history of specific defects and elements, and the ability to enhance collaboration of bridge stakeholders.

Within the framework of the BIDM, the accuracy of AR-supported measurements is investigated. To prove the accuracy, AR-measurements are aligned with conventional measure tools used for bridge inspections. The performed case study is comprised of 141 measurement data pairs of which 88.65 % deviate less or equal to 0.5 inch, which is inside the deviation range for inspecting concrete structures. It can be stated that AR-supported measurements are as accurate as analog measurements. Therefore, they are applicable for inspecting concrete bridges.

The interaction of both techniques investigated in this thesis enhances the visual bridge inspection. It is proven that the human-centered approach is simply applicable to current inspection procedures.



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