PhD Oral Exam - Dena Shamsollahi, 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

In complex and dynamic construction sites, efficient progress monitoring and reporting play an important role in minimizing schedule delays and cost overruns. Such reporting requires detailed and accurate records from job sites to help project managers in comparing project’s current state to its as-planned state. Manual traditional progress reporting is time-consuming, costly, labour-intensive, and error-prone. In recent years, advancements in technologies and methods have been introduced in an effort to overcome the challenges of manual methods and to automate the processes of progress monitoring and reporting. These introduced levels of automation still lack capabilities to provide complete and accurate information about the project’s current status and available resources on job sites. To address these challenges, this thesis introduces a novel framework for automated progress reporting in construction. This framework provides detailed information for each tracked building element, enabling the identification of its current status and the generation of timely progress reports. The developments integrated into the framework focus on challenges associated with congested mechanical components in indoor environments. Monitoring these components is crucial because their complex and time-consuming installation procedures can lead to project delays.

The developed framework consists of three main modules: (i) Object Recognition (ii) Object Localization, (iii) Integrated Object Recognition and Localization. In the “Object Recognition” module, two deep learning algorithms, YOLACT++ and Mask R-CNN, were utilized in processing digital images captured at construction sites for the automated recognition of tracked building elements. YOLACT++ proved superior to Mask R-CNN and was accordingly utilized in the developed framework. In the “Object Localization” module, a Real-time Locating System (RTLS) is utilized to identify the location of each recognized element along with its ID. The Ultra-wideband (UWB) system was selected as an RTLS, and different laboratory and field experiments were conducted to validate the UWB system’s localization performance. Finally, in the “Integrated Object Recognition and Localization” module, a user-friendly application was developed to integrate the outputs from the YOLACT++ model and the UWB system and automatically generate status reports of tracked elements. These reports include visual and location information, along with the unique ID of each element.

The framework was tested and validated using 3,632 images. The results demonstrate good performance and effectiveness of the developed framework under challenging conditions; yielding recognition accuracy of close to 85% in precision and recall for HVAC duct and slightly less than that for pipes. Similar performance was achieved in localization, yielding errors ranging from 0.03 to 1.22 meters in two-dimensional (2D) coordinates and from 0.15 to 1.6 meters in three-dimensional (3D) coordinates in the field test. The developed framework can be easily extended to other building elements, and the excel format of its output can facilitate linkage with Building Information Modeling (BIM) systems.