Citation

Xu X, Zou PXW. Safety Sci. 2021; 144: e105481.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105481

PMID

unavailable

Abstract

Safety in construction continues to remain as the 3rd most serious occupational problem worldwide. Previous research mainly used questionnaire surveys or interviews to obtain "cross-sectional" data, which may be difficult to extract patterns and gain new knowledge for improving safety performance. This research aims to discover the characteristics, patterns, mechanisms and knowledge from mining a large injury dataset, and to develop strategies for mitigating safety risks, and reducing accidents and injuries so that to improve construction safety performance. A large set of injury and incident data recorded by construction companies over a period of 11 years was used in this research. Statistical analysis, visualisation analysis and association rule mining (ARM) methods were applied to mine and analyse this large dataset. "Injury time", "injured body locations, organs and systems", "age distribution of injured", "causes, nature and relationships of injury, and injured body parts" "changing trends of incidents", were analysed in depth to discover new safety knowledge. Based on the knowledge and mechanism discovered, this study proposed five strategies for improving construction safety performance covering safety skills, safety awareness, safety protection and emergency response. This research would be valuable to both researchers and practitioners. Researchers would benefit from gaining a deeper understanding of the characteristics of construction safety injuries, application of data mining methods, and relevant future research directions, while practitioners would benefit by learning about the strategies proposed to mitigate injury risks and prevent incidents.

Language: en

Keywords

Association rule mining; Construction safety; Incident; Injury; Knowledge discovery; Visualisation