Understanding Construction Workers’ Risk Perception Using Neurophysiological Responses
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
In the dynamic construction environment, workers’ safety heavily depends on their ability to effectively perceive and react to hazards. Accordingly, studies have assessed the status of workers’ risk perception using advanced technologies. However, these studies have mainly focused on whether risks are perceived rather than how they are perceived. Recognizing the need for effective safety interventions that address risk-perception failures, it becomes crucial to not only classify workers’ risk-perception states but also to delve into the underlying processes of their risk perception. To address this research gap, this study examines the critical aspect of risk perception in construction safety by employing functional near-infrared spectroscopy (fNIRS) and 360° panoramas from actual construction sites to assess workers’ cognitive processes during hazard identification. Classifiers were developed using the AutoML method, and 15 advanced machine learning algorithms were compared to identify the highest-performing model. This model would then be utilized to understand the risk-perception process by incorporating the feature-importance technique. The results indicate that CatBoost emerged as the most effective classifier, achieving an accuracy rate of 90.3%. Additionally, the results identify significant brain activations in four anatomical locations: the prefrontal cortex, frontal eye fields, primary motor cortex, and primary auditory cortex. Notably, there is a significant correlation between these areas, emphasizing the importance of both visual and auditory cue perception in shaping workers’ situational awareness. This research highlights the potential of neuroimaging fNIRS in improving construction safety, and the importance of auditory perception in hazard identification, offering insights that could enhance the effectiveness of safety training programs.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The National Science Foundation is thanked for supporting the research reported in this paper (2049711). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation and supporting construction company. The authors also would like to thank the workers and safety managers who participated in and supported this study.
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Journal of Computing in Civil Engineering
Volume 38 • Issue 6 • November 2024
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© 2024 American Society of Civil Engineers.
History
Received: Dec 23, 2023
Accepted: Jun 4, 2024
Published online: Aug 30, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 30, 2025
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