A field survey of hand–arm vibration exposure in the UK utilities sector

David John Edwards, Iain Rillie, Nicholas Chileshe, Joesph Lai, M. Reza Hosseini, Wellington Didibhuku Thwala

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Purpose: Excessive exposure to HAV can lead to hand–arm vibration syndrome (HAVS) which is a major health and well-being issue that can irreparably damage the neurological, vascular and muscular skeletal system. This paper reports upon field research analysis of the hand–arm vibration (HAV) exposure levels of utility workers in the UK construction sector when operating hand-held vibrating power tools. Design/methodology/approach: An empirical epistemological lens was adopted to analyse primary quantitative data on the management of hand-held tool trigger times (seconds) collected from field studies. To augment the analysis further, an interpretivist perspective was undertaken to qualitatively analyse interviews held with the participating company's senior management team after field study results. This approach sought to provide further depth and perspective on the emergent numerical findings. Findings: The findings reveal that none of the operatives were exposed above the exposure limit value (ELV) and that 91.07% resided under the exposure action value (EAV). However, the Burr four parameter probability model (which satisfied the Anderson–Darling, Kolmogorov–Smirnov and chi-squared goodness of fit tests at (Formula presented.) 0.01, 0.02, 0.05, 0.1 and 0.2 levels of significance) illustrated that given the current data distribution pattern, there was a 3% likelihood that the ELV will be exceeded. Model parameters could be used to: forecast the future probability of HAV exposure levels on other utility contracts and provide benchmark indicators to alert senior management to pending breaches of the ELV. Originality/value: HAV field trials are rarely conducted within the UK utilities sector, and the research presented is the first to develop probability models to predict the likelihood of operatives exceeding the ELV based upon field data. Findings presented could go some way to preserving the health and well-being of workers by ensuing that adequate control measures implemented (e.g. procuring low vibrating tools) mitigate the risk posed.

Original languageEnglish
Pages (from-to)2179-2198
Number of pages20
JournalEngineering, Construction and Architectural Management
Volume27
Issue number9
DOIs
Publication statusPublished - 8 Oct 2020

Keywords

  • Hand-arm vibration
  • Health and well-being
  • Industry 4.0
  • Probability models
  • Utilities industry

ASJC Scopus subject areas

  • Architecture
  • Civil and Structural Engineering
  • Building and Construction
  • Business, Management and Accounting(all)

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