Off-road truck-related accidents in U.S. mines

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@Article{Dindarloo:2016:JSR,
  author =       "Saeid R. Dindarloo and Jonisha P. Pollard and 
                 Elnaz Siami-Irdemoosa",
  title =        "Off-road truck-related accidents in U.S. mines",
  journal =      "Journal of Safety Research",
  volume =       "58",
  pages =        "79--87",
  year =         "2016",
  ISSN =         "0022-4375",
  DOI =          "doi:10.1016/j.jsr.2016.07.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022437516301347",
  abstract =     "AbstractIntroduction Off-road trucks are one of the
                 major sources of equipment-related accidents in the
                 U.S. mining industries. A systematic analysis of all
                 off-road truck-related accidents, injuries, and
                 illnesses, which are reported and published by the Mine
                 Safety and Health Administration (MSHA), is expected to
                 provide practical insights for identifying the accident
                 patterns and trends in the available raw database.
                 Therefore, appropriate safety management measures can
                 be administered and implemented based on these accident
                 patterns/trends. Methods A hybrid
                 clustering-classification methodology using K-means
                 clustering and gene expression programming (GEP) is
                 proposed for the analysis of severe and non-severe
                 off-road truck-related injuries at U.S. mines. Using
                 the GEP sub-model, a small subset of the 36 recorded
                 attributes was found to be correlated to the severity
                 level. Results Given the set of specified attributes,
                 the clustering sub-model was able to cluster the
                 accident records into 5 distinct groups. For instance,
                 the first cluster contained accidents related to
                 minerals processing mills and coal preparation plants
                 (91percent). More than two-thirds of the victims in
                 this cluster had less than 5 years of job experience.
                 This cluster was associated with the highest percentage
                 of severe injuries (22 severe accidents, 3.4percent).
                 Almost 50percent of all accidents in this cluster
                 occurred at stone operations. Similarly, the other four
                 clusters were characterized to highlight important
                 patterns that can be used to determine areas of focus
                 for safety initiatives. Conclusions The identified
                 clusters of accidents may play a vital role in the
                 prevention of severe injuries in mining. Further
                 research into the cluster attributes and identified
                 patterns will be necessary to determine how these
                 factors can be mitigated to reduce the risk of severe
                 injuries. Practical application Analyzing injury data
                 using data mining techniques provides some insight into
                 attributes that are associated with high accuracies for
                 predicting injury severity.",
  keywords =     "genetic algorithms, genetic programming, Off-road
                 mining trucks, Fatalities and injuries, K-means
                 clustering, Classification",
}

Genetic Programming entries for Saeid R Dindarloo Jonisha P Pollard Elnaz Siami-Irdemoosa

Citations