TY - JOUR
T1 - Fine-Kinney fuzzy-based occupational health risk assessment for Workers in different construction trades
AU - Li, Hongyang
AU - Wang, Yousong
AU - Chong, Dan
AU - Rajendra, Darmicka
AU - Skitmore, Martin
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/12/1
Y1 - 2024/12/1
N2 - This paper aims to enhance occupational health risk assessment for construction workers by introducing and validating two innovative models: the Occupational Health Risk Assessment Hierarchy Model (OHRAHM) and the Occupational Health Hazard Factor Risk Assessment Model (OHHFRAM). Utilizing the Fine-Kinney method (FKM), fuzzy sets, and the fuzzy inference system (FIS), these models provide a nuanced understanding of health risks in various construction trades. The models are demonstrated and validated in a specific region of China, including applying the FKM to different construction work types and evaluating occupational health across multiple trades. The principal results show the potential of OHRAHM and OHHFRAM in assessing risk magnitudes for various health factors, offering valuable insights for risk classification and proposing effective control measures. This paper addresses the need for improved health risk assessment models in the construction industry.
AB - This paper aims to enhance occupational health risk assessment for construction workers by introducing and validating two innovative models: the Occupational Health Risk Assessment Hierarchy Model (OHRAHM) and the Occupational Health Hazard Factor Risk Assessment Model (OHHFRAM). Utilizing the Fine-Kinney method (FKM), fuzzy sets, and the fuzzy inference system (FIS), these models provide a nuanced understanding of health risks in various construction trades. The models are demonstrated and validated in a specific region of China, including applying the FKM to different construction work types and evaluating occupational health across multiple trades. The principal results show the potential of OHRAHM and OHHFRAM in assessing risk magnitudes for various health factors, offering valuable insights for risk classification and proposing effective control measures. This paper addresses the need for improved health risk assessment models in the construction industry.
UR - http://www.scopus.com/inward/record.url?scp=85202994056&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2024.105738
DO - 10.1016/j.autcon.2024.105738
M3 - Article
AN - SCOPUS:85202994056
SN - 0926-5805
VL - 168 (Part A)
SP - 1
EP - 10
JO - Automation in Construction
JF - Automation in Construction
M1 - 105738
ER -