Development of Data-Driven Influence Model to Relate the Workplace Environment to Human Error
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 3
Abstract
Because human error plays a direct role in accidents, studying the causal relationship between the environment and human error is essential to prevent mishaps. However, these relationships have been explored solely using bivariate statistical analysis and thus require more intermediate factors to emphasize the need for monitoring and controlling human error by improving the workplace environment. Moreover, prevalent studies rely heavily on expert experience, which is subjective and creates potential estimation noise. In this study, the mechanism whereby environmental factors influence behavior and its associate factors is learned with an algorithm using a Bayesian network structure. Rather than being simply data-driven, the algorithm initiates learning from prior knowledge, the theoretical causal chain in the cognitive reliability and error analysis method (CREAM), and revises the learning approach against safety inspection data if necessary. The learned Bayesian network shows that human error and incorrect sequencing result from a combination of limited cognitive functions and improper spatial/workmanship arrangements caused by equipment defects, improper design, and management problems. Bridging the gaps in previous studies, the action interface revealed by this study is useful for on-site quality control.
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Data Availability Statement
Data analyzed during the study were provided by a third party. Requests for data should be directed to the provider indicated in the acknowledgements. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
The authors thank the Natural Science Foundation of China (No. 51578317) and the United Technologies Corporation (No. 20153000259) for their support of this study. The authors specially thank United Technologies Corporation for providing the data used in this study. The authors are also grateful for input from industry professionals who participated in this research.
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Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 3 • March 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Apr 11, 2017
Accepted: Sep 12, 2017
Published online: Jan 6, 2018
Published in print: Mar 1, 2018
Discussion open until: Jun 6, 2018
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