PhD Oral Exam - Hassan Bardareh, Building Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

This research investigates the application of indoor object localization to enhance tracking and progress reporting in construction projects by automating the generation of onsite inspection reports and location identification of project components. In this study, “inspection reports” refers to the documents used to monitor and record the progress of installed project components and track their target locations. Indoor object tracking is challenging due to the complex nature of construction environments, which typically are congested and contain many obstacles. Moreover, translating object-tracking information into meaningful progress reports is inherently challenging. To address these challenges, this study explores the integrated use of advanced technologies, such as RTLS and LiDAR, for location identification of the target objects. The study includes four main streams: (1) developing an object localization method based on integrated RTLS technologies and using trilateration techniques for 2D and 3D localization in indoor spaces, (2) integrating the object tracking with the progress tracking leveraging the MSI and the QSI, and employing a cloud-based BIM platform for data collection and visualization, (3) integrating RTLS with point cloud data to refine the 3D object detection and localization functions, and (4) developing a digital-twin platform for automated generation of onsite inspection reports and visualization of the location and status of the objects associated with indoor construction operations. These reports are visualized through a bi-directional construction twin dashboard, facilitating ready access to progress-related information for site managers. The methods developed are validated through laboratory experiments and a case study conducted at a job site. In the laboratory experiments, the RTLS demonstrates an accuracy of approximately 0.52 m and 1.15 m, respectively, for 2D and 3D object localization. The 3D localization accuracy for the integrated RTLS and point cloud data, meanwhile, is found to be 27 cm. The case study also validates the effectiveness of the introduced indices in reporting the progress of the installation of components in mechanical rooms as part of a swimming pool construction project.