TY - JOUR
T1 - Development of an early-warning system for site work in hot and humid environments
T2 - A case study
AU - Yi, Wen
AU - Chan, Albert P.C.
AU - Wang, Xiangyu
AU - Wang, Jun
N1 - Funding Information:
This project is funded by two grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Project No. PolyU510409 and PolyU510513 ). The support from the Hong Kong Polytechnic University's Institute of Textiles and Clothing (ITC) is deeply appreciated. The research team is also indebted to the technical support from technicians of the Hong Kong Polytechnic University and the Hong Kong Institute of Education. In particular, the participation of volunteers in this experimental study is gratefully acknowledged. This paper forms part of the research project titled “Experimental research on health and safety measures for working in hot weather”, from which other deliverables will be produced with different objectives/scopes but sharing common background and methodology. The authors also wish to acknowledge the contributions of other team members including Dr Michael Yam, Dr Daniel Chan, Prof Joanne Chung, Prof Esmond Mok, Dr Geoffrey Shea, Dr Min Wu, Dr Herbert Biggs, Dr Donald Dingsdag, and Miss Alice Guan.
Publisher Copyright:
© 2015 Elsevier B.V. All rights reserved.
PY - 2016/2/1
Y1 - 2016/2/1
N2 - This study presents an early-warning system for working in hot and humid environment. The developed system can monitor workers' heat strain level when they have to work under such hostile conditions continuously. Health alert messages with corresponding intervention measures will be prompted to workers to safeguard their wellbeing. Heat strain is evaluated by a subjective index perception rating of perceived exertion (RPE) and an objective heat strain indicator heart rate. A database containing 550 sets of synchronized work-related, environmental, and personal data were used to construct the prediction model. Artificial neural networks were applied to forecast the RPE of construction workers. Statistical measures including MAPE, RMSE and R2 confirm that the established model is good fitting with high accuracy. The proposed system could be automated by integrating smart sensor technology, location tracking technology, and information communication technology, which could be in the form of GSM based environmental sensor, smart bracelet, and smart phone application, to protect the wellbeing for those who have to work in hot and humid conditions.
AB - This study presents an early-warning system for working in hot and humid environment. The developed system can monitor workers' heat strain level when they have to work under such hostile conditions continuously. Health alert messages with corresponding intervention measures will be prompted to workers to safeguard their wellbeing. Heat strain is evaluated by a subjective index perception rating of perceived exertion (RPE) and an objective heat strain indicator heart rate. A database containing 550 sets of synchronized work-related, environmental, and personal data were used to construct the prediction model. Artificial neural networks were applied to forecast the RPE of construction workers. Statistical measures including MAPE, RMSE and R2 confirm that the established model is good fitting with high accuracy. The proposed system could be automated by integrating smart sensor technology, location tracking technology, and information communication technology, which could be in the form of GSM based environmental sensor, smart bracelet, and smart phone application, to protect the wellbeing for those who have to work in hot and humid conditions.
KW - Artificial neural networks (ANNs)
KW - Construction industry
KW - Early-warning system
KW - Heat stress
KW - Hong Kong
KW - Occupational health and safety (OHS)
UR - http://www.scopus.com/inward/record.url?scp=84948172068&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2015.11.003
DO - 10.1016/j.autcon.2015.11.003
M3 - Journal article
AN - SCOPUS:84948172068
SN - 0926-5805
VL - 62
SP - 101
EP - 113
JO - Automation in Construction
JF - Automation in Construction
ER -