Development of an early-warning system for site work in hot and humid environments: A case study

Wen Yi, Ping Chuen Chan, Xiangyu Wang, Jun Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

57 Citations (Scopus)

Abstract

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 R2confirm 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.
Original languageEnglish
Pages (from-to)101-113
Number of pages13
JournalAutomation in Construction
Volume62
DOIs
Publication statusPublished - 1 Feb 2016

Keywords

  • Artificial neural networks (ANNs)
  • Construction industry
  • Early-warning system
  • Heat stress
  • Hong Kong
  • Occupational health and safety (OHS)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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