Identification of accident-injury type and bodypart factors from construction accident reports: A graph-based deep learning framework |
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| Affiliation: | 1. National Center of Technology Innovation for Digital Construction, Huazhong University of Science & Technology, Wuhan, Hubei, China;2. School of Civil and Hydraulic Engineering, Huazhong University of Science & Technology, China;1. College of Management and Economics, Tianjin University, Tianjin 300072, China;2. Department of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, Canada;3. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore;3. Department of Mechanical and Electromechanical Engineering, National ILan University, ILan 26041, Taiwan;4. Huazhong University of Science and Technology – Wuxi Research Institute, Wuxi 214000, China;5. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430072, China;1. School of Economics and Management, Beihang University, Beijing 100191, China;2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing 100191, China;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China;1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China |
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| Abstract: | Accident reports provide information to understand why and how events occur. Learning from past accident reports is critical for preventing accidents or injuries in construction safety management. However, there are two issues: (1) manual analysis of such accident reports is time-consuming and labor-intensive; and (2) previous research mainly focused on analyzing the causal factors of accidents. Not much research concentrates on the injury effect in an accident and the influential relationship between accident cause and injury effect. To tackle this problem, a graph-based deep learning framework is proposed to identify accident-injury type and bodypart factors automatically to enable managers to make timely and better-informed decisions to prevent accidents and injuries for on-site safety. In this framework, a graph-based deep learning approach (specifically, the Graph Convolutional Network) is developed to automatically classify accident reports labeled with accident_type and injury_type, whereas the traversal method is developed to identify the bodypart factors. To further intuitively visualize these safety risk factors (e.g., accident_type, injury_type, and bodypart factors), the co-occurrence networks are drawn to further intuitively reveal the interdependency in accident-injury and injury-bodypart types respectively. From the perspective of theoretical and practical contributions, the framework proposed in this study not only represents a substantial data-driven advancement in construction accident report classification and keyword extraction tasks, but also enables managers to get knowledge of construction safety performance (i.e., accident causes and injury effects) and further formulate corresponding strategies to prevent accidents and injuries in on-site safety management. |
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| Keywords: | Construction safety management Accident reports Graph-based deep learning Text classification Keyword extraction Safety risk factors |
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