Project completed in 2023
Level: Ph.D.

Development of a Framework for the Implementation of BIM-Enabled Diminished Reality in the AEC-FM Industry

Abstract

This research project aims to create a framework for the integration of Building Information Modeling (BIM) with Diminished Reality (DR) in the Architecture, Engineering, Construction, and Facility Management (AEC-FM) sector. DR is a real-time technique for visually removing unwanted physical objects, enhancing visualizations in Augmented Reality (AR) scenarios. Although both BIM and DR have garnered attention individually, their synergistic potential remains underexplored. This research endeavors to bridge this gap by establishing a robust framework that leverages BIM’s data management, visualization, and information-rich capabilities to enhance DR applications. The framework will be designed to improve monitoring, management, and planning tasks within AEC-FM, offering an innovative approach to real-time visualization and information retrieval. By providing a structured methodology and insights into the practical challenges and benefits, this thesis contributes to advancing the adoption of BIM-enabled DR in the AEC-FM industry.

Project results

In this thesis, we have achieved several significant research results and also anticipate additional expected outcomes: 1. BIM-Enabled DR Framework: The development of a framework for integrating Building Information Modeling (BIM) with Diminished Reality (DR) in the Architecture, Engineering, Construction, and Facility Management (AEC-FM) industry has been successfully achieved. This framework serves as a structured approach to seamlessly combine these technologies, enabling real-time object removal and enhancing visualizations. 2. Enhanced Real-time Visualization: The framework has significantly improved the visualization capabilities within the AEC-FM sector, resulting in more precise and detailed visual representations of construction sites and facilities. This advancement contributes to better decision-making, monitoring, and planning processes.
3. Practical Applications: The research has identified and explored various practical applications of BIM-enabled DR in the AEC-FM industry. These applications encompass a wide range of tasks, including construction project monitoring, facility management, landscape development, and more. The successful implementation of this technology has provided practical solutions to longstanding challenges within the industry, improving efficiency, safety, and accuracy in various tasks. 4. Future Outcomes: The integration of artificial intelligence (AI) and digital twins represents the next frontier in our research. By incorporating AI algorithms and harnessing the power of digital twins, we aim to further enhance the capabilities of our BIM-Enabled Diminished Reality framework.

Project contributions

This study will provide an opportunity to advance the understanding of DR as a new visualization technique by exploring the DR literature and demonstrating how different methods can be used in various applications. The other contribution will be exploring the advantages of BIM as the main source of digital information of the physical environments to assist DR for rendering and background information retrieval. In the industrial context, this research offers practical solutions to long-standing challenges. The identification and exploration of various applications for BIM-Enabled DR equip professionals in the AEC-FM sector with tangible tools to enhance real-time monitoring, planning, and management tasks.

Publications

Publications from this project are available below:

Eskandari, Roghieh; Motamedi, Ali, Diminished Reality in Architectural and Environmental design: Literature Review of Techniques, Applications, and Challenges. 38th International Symposium on Automation and Robotics in Construction (ISARC 2021).

Eskandari, Roghieh; Motamedi, Ali, Visibility Enhancement of Crane Operators Using BIM-Based Diminished Reality. 23rd International Conference on Construction Applications of Virtual Reality (CONVR 2023).

Research team

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Team

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Partners

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