Developing a dump truck drivers occupational fatigue assessment model as hauling workplace accidents prevention: Study case at Indonesia’s coal mining company

Aryasatya Adyatama, Danu Hadi Syaifullah, Boy Nurtjahyo Moch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

One of the biggest financial loss due to workplace accidents at coal mining companies is caused by dump truck drivers’ sleepiness during hauling activities, resulting in loss of steering control. Drowsiness is an indicator of occupational fatigue as a body reaction to prevent another serious illness. An efficient fatigue management policy is needed to reduce the number of coal mining workplace accidents. Health and safety department should assess the driver’s physical condition is fit to work before hauling activity is started as a preventive program. This study was analyzing the physical factors that significantly influence the dump truck drivers occupational fatigue. A mathematical model to assess the probability of whether dump truck drivers are suspected of having occupational fatigue or not is the result of this study. This study used logistic regression statistical method to give an accurate occupational fatigue calculation which can be implemented in Indonesia coal mining companies.

Original languageEnglish
Title of host publication2019 the 5th International Conference on Industrial and Business Engineering, ICIBE 2019
PublisherAssociation for Computing Machinery
Pages164-168
Number of pages5
ISBN (Electronic)9781450376532
DOIs
Publication statusPublished - 27 Sep 2019
Event5th International Conference on Industrial and Business Engineering, ICIBE 2019 - Hong Kong, Hong Kong
Duration: 27 Sep 201929 Sep 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference5th International Conference on Industrial and Business Engineering, ICIBE 2019
CountryHong Kong
CityHong Kong
Period27/09/1929/09/19

Keywords

  • Coal Mining
  • Health and Safety
  • Logistic Regression
  • Occupational Fatigue
  • Workplace Accidents

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