Understanding Construction Workers’ Risk Perception Using Neurophysiological Responses-Reference-Cited by-同舟云学术

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Understanding Construction Workers’ Risk Perception Using Neurophysiological Responses

Published:2024-11 Issue:6 Volume:38 Page:
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Lee Kyeongsuk¹,Pooladvand Shiva¹,Esmaeili Behzad²,Hasanzadeh Sogand³

Affiliation:

1. Ph.D. Candidate, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907.

2. Associate Professor, School of Civil Engineering and Industrial Engineering, Purdue Univ., 315 N. Grant St., West Lafayette, IN 47907.

3. Assistant Professor, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JCCEE5.CPENG-5906

Reference96 articles.

1. Enhancing Construction Hazard Recognition with High-Fidelity Augmented Virtuality

2. Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

3. Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models

4. Risk Perception and Experience: Hazard Personality Profiles and Individual Differences

5. Benerradi J. H. A. Maior A. Marinescu J. Clos and M. L. Wilson. 2019. “Exploring machine learning approaches for classifying mental workload using fNIRS data from HCI tasks.” In Proc. Halfway to the Future Symp. 2019 1–11. New York: Association for Computing Machinery.

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