Dr. Chung-Suk Cho has more than 22 years of combined industry and university experience in the field of construction and project management. Prior to joining Khalifa University, he worked as an Assistant Professor in the Department of Engineering Technology and Construction Management at the University of North Carolina at Charlotte, as well as North Carolina Agricultural and Technical State University at Greensboro. He formerly worked as a project manager for Fluor Corporation at Rumford, Rhode Island.
Dr. Cho’s research interest spans such topics as project scope definitions, front-end planning, smart construction, construction safety, engineering education, and sustainable construction particularly focusing on construction CO2 emission reduction. He has managed several research projects, as well as external research funding, from the Abu Dhabi Department of Education and Knowledge (ADEK) and various US federal and state agencies, such as NSF, OSHA (US Department of Labor), ASCE Construction Institute, and NCDOT. He is a member of ASCE, an Associate Editor of KSCE Journal of Civil Engineering, and an OSHA construction outreach trainer.
This research develops a novel technique that monitors the workers whether they are complying with a safety standard of the Personal Fall Arrest System (PFAS). The research establishes a real time detection algorithm based on a Convolutional Neural Network (CNN) model in order to detect two main components of the PFAS that are, safety harness and life-line, in addition to a standard safety measure of using a safety helmet. The YOLOv3 algorithm is adopted for a deep learning network used to train the desired model.
This research developed a novel system integrating deep learning and drones to monitor workers in real-time when performing at-height activities. Specifically, a pre-trained deep learning model was used to detect Personal Fall Arrest System components (e.g., safety harness, lifeline, and helmet). The drone was utilized to take images and videos from the construction site, and the data were relayed to the model to detect safety violations.
