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2016 ; 58
(ä): 79-87
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Off-road truck-related accidents in U S mines
#MMPMID27620937
Dindarloo SR
; Pollard JP
; Siami-Irdemoosa E
J Safety Res
2016[Sep]; 58
(ä): 79-87
PMID27620937
show ga
INTRODUCTION: 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 (91%). More than two-thirds of the
victims in this cluster had less than 5years of job experience. This cluster was
associated with the highest percentage of severe injuries (22 severe accidents,
3.4%). Almost 50% 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.
|*Mining
[MESH]
|Accidents, Traffic/*statistics & numerical data
[MESH]
|Cluster Analysis
[MESH]
|Humans
[MESH]
|Motor Vehicles/*statistics & numerical data
[MESH]
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