TY - JOUR
T1 - A field survey of hand–arm vibration exposure in the UK utilities sector
AU - Edwards, David John
AU - Rillie, Iain
AU - Chileshe, Nicholas
AU - Lai, Joesph
AU - Hosseini, M. Reza
AU - Thwala, Wellington Didibhuku
N1 - Funding Information:
The lead author wishes to thank the utility company's safety and plant services departments for supporting this research and also workers within the company who participated in the study. Without your support, this work could not have been made possible. Thank you also to professor Peter Love, distinguished professor, Curtin University who gave the lead author a harsh (but fair) reality check regarding the lead author's own research and helped guide the use of this analysis technique ? this jolt has spurred me on!
Publisher Copyright:
© 2020, Emerald Publishing Limited.
PY - 2020/10/8
Y1 - 2020/10/8
N2 - 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.
AB - 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.
KW - Hand-arm vibration
KW - Health and well-being
KW - Industry 4.0
KW - Probability models
KW - Utilities industry
UR - http://www.scopus.com/inward/record.url?scp=85083840366&partnerID=8YFLogxK
U2 - 10.1108/ECAM-09-2019-0518
DO - 10.1108/ECAM-09-2019-0518
M3 - Journal article
AN - SCOPUS:85083840366
SN - 0969-9988
VL - 27
SP - 2179
EP - 2198
JO - Engineering, Construction and Architectural Management
JF - Engineering, Construction and Architectural Management
IS - 9
ER -