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Title:Automated monitoring and systemic analysis of workers' safety behavior in construction operations
Author(s):Han, Sang Uk
Director of Research:Pena-Mora, Feniosky A.
Doctoral Committee Chair(s):Liu, Liang Y.
Doctoral Committee Member(s):Pena-Mora, Feniosky A.; Lee, SangHyun; El-Rayes, Khaled A.; Golparvar-Fard, Mani
Department / Program:Civil & Environmental Eng
Discipline:Civil Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Construction management
safety
behavioral safety
motion capture and recognition
simulation
complex systems
Abstract:In construction, workforces are an essential element of safety management. Previous studies found that about 80%–90% of accidents are strongly associated with workers’ unsafe actions and errors, which are affected by safety-related factors (e.g., safety program, safety culture, production pressure). Behavior measurement also turns out to be significantly correlated to safety outcomes (e.g., incident rates). To this end, an in-depth understanding of workers’ behavior has tremendous potential to contribute to the prevention of construction injuries and illnesses. However, there are immense difficulties regarding worker observation in construction, which is the single most significant element in managing workers’ behavior. These difficulties are three-fold: (1) field observation is time-consuming, painstaking, and error-prone due to the complexity of workforce and workplace on a jobsite; (2) training workers for observation is expensive and inefficient for the short tenure of construction workers, particularly hired by various subcontractors; and (3) workers’ active participation is required for peer-to-peer observation, survey, and interview. In an effort to provide a robust and automated means for worker observation, this study proposes a framework of vision-based unsafe posture and action detection for human behavior monitoring. The framework consists of: (1) the identification of critical unsafe actions and postures; (2) the collection of relevant motion templates (i.e., identified critical unsafe actions); (3) the extraction of a 3D human skeleton model from videos collected with a stereo camera system (e.g., a 3D camcorder, multiple cameras); and (4) the detection of unsafe actions using the motion templates and 3D skeleton models. As a case study, experimental studies were undertaken to evaluate each process of the framework. This research thus opens up the possibility of micro-level motion tracking and recognition with video cameras to identify the frequency and types of workers’ unsafe actions on jobsites. The resulting information will serve as: (1) preliminary information for providing workers with direct feedback on their behavior; (2) a positive safety performance measurement to evaluate ongoing safety management; and (3) data to analyze the impact of safety-related factors (e.g., schedule pressure, rework, safety training) on workers’ behavior. In particular, the proposed approach will allow one to provide feedback on workers’ safety behavior, to foster communications between a manager and workers through the feedback process, and eventually to contribute to the creation of a safe climate on a jobsite.
Issue Date:2013-08-22
URI:http://hdl.handle.net/2142/45601
Rights Information:Copyright 2013 Sang Uk Han
Date Available in IDEALS:2013-08-22
2015-08-22
Date Deposited:2013-08


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