Nath, N D (2021) Human-centered computing and visual analytics for future of work in construction. PhD thesis, Texas A&M University, USA.
Abstract
Artificial intelligence (AI) is revolutionizing various systems within the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) domains. The rapid advancements in computational methods, engineering knowledge, and sensor technologies is transforming the current construction practices that are heavily reliant on human intervention. Therefore, visionaries are foreseeing that future construction works will be collaborated by humans and machines which will lead to unprecedented socio-economic outcomes in the safety, health, and productivity of construction workers. This Dissertation aims to advance the fundamental knowledge for effectively implementing human-machine collaboration in the construction site. Particularly, the ultimate objective of this Dissertation is to design AI-based autonomous systems for continuously monitoring workplace safety and productivity. Toward this goal, firstly, a content retrieval scheme is designed to analyze a large volume of construction imagery at a rapid speed. Next, an object recognition framework is developed to detect construction-related objects from digital images or videos in real-time. By further extending this framework, an automated safety monitoring system is subsequently designed to verify workers’ compliance with the requirements related to personal protective equipment (PPE). Next, an AI-enabled image enhancement technique is developed to improve the quality of visual data to achieve better performance from the detection models in the preceding steps. Finally, an active vision system is proposed that enables an autonomous camera to intelligently navigates through a jobsite to monitor objects of interest for their safety and productivity.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Paal, S and Behzadan, A H |
| Uncontrolled Keywords: | protective equipment; construction site; equipment; artificial intelligence; collaboration; compliance; computing; facilities management; monitoring; safety; productivity; construction worker |
| Index terms: | monitoring, personal protective equipment, construction work, dissertation, construction worker, protective equipment, artificial intelligence, object recognition, productivity, collaboration, computing, facilities management, workplace safety, compliance, construction site |
| Subjects: | management, occupational health and safety management, control systems, computing systems, work location, artificial intelligence, health safety and environment, computer vision, occupational health, research dissemination and communication, practitioner, operations management |
| Topics: | Project Management, Health and Safety, Digital Applications, Organizational Design, Site Management, Research Practice, Business Strategy, Roles and Professions |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here