Patterns in Mining Haul Truck Accidents

Drury, Colin G.; Porter, William L.; Dempsey, Patrick G.

doi:10.1177/1071181312561420

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…An initial set of patterns was developed, and then these were refined following coding of the entire sample of 133 haul truck fatalities that occurred between 1995 and 2010 (Drury, Porter, and Dempsey 2012). The highest-level classification divided the fatalities into driving (first-level subcategories of ‘loss of control’, ‘ground fails’ and ‘two-vehicle collision’) and non-driving (first-level subcategories of ‘unexpected movement’, ‘falls from vehicle’ and ‘hit by other vehicle’).…”

Section: Methods and Findingsmentioning

confidence: 99%

“…The highest-level classification divided the fatalities into driving (first-level subcategories of ‘loss of control’, ‘ground fails’ and ‘two-vehicle collision’) and non-driving (first-level subcategories of ‘unexpected movement’, ‘falls from vehicle’ and ‘hit by other vehicle’). The refined classifications and further sub-categorisation is discussed in more detail by Drury, Porter, and Dempsey (2012). Like the non-fatal analyses described above, the accident patterns discovered were used to develop audit items and modules intended to prevent similar occurrences in the future.…”

Section: Methods and Findingsmentioning

confidence: 99%

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Development of ergonomics audits for bagging, haul truck and maintenance and repair operations in mining

et al. 2017

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The development and testing of ergonomics and safety audits for small and bulk bag filling, haul truck and maintenance and repair operations in coal preparation and mineral processing plants found at surface mine sites is described. The content for the audits was derived from diverse sources of information on ergonomics and safety deficiencies including: analysis of injury, illness and fatality data and reports; task analysis; empirical laboratory studies of particular tasks; field studies and observations at mine sites; and maintenance records. These diverse sources of information were utilised to establish construct validity of the modular audits that were developed for use by mine safety personnel. User and interrater reliability testing was carried out prior to finalising the audits. The audits can be implemented using downloadable paper versions or with a free mobile NIOSH-developed Android application called ErgoMine. Practitioner Summary The methodology used to develop ergonomics audits for three types of mining operations is described. Various sources of audit content are compared and contrasted to serve as a guide for developing ergonomics audits for other occupational contexts.

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Section: Methods and Findingsmentioning

confidence: 99%

Section: Methods and Findingsmentioning

confidence: 99%

Development of ergonomics audits for bagging, haul truck and maintenance and repair operations in mining

et al. 2017

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“…Analysis of workplace injuries has been heavily utilized as a means to determine high-risk tasks, prioritize workplace redesign, and determine areas of concern for worker safety in many industries including healthcare, construction, retail and services, and mining (Cato, Olson, & Studer, 1989; Drury, Porter, & Dempsey, 2012; Mardis & Pratt, 2003; Moore, Porter, & Dempsey, 2009; Pollard, Heberger, & Dempsey, 2014; Schoenfisch, Lipscomb, Shishlov, & Myers, 2010; Turin, Wiehagen, Jaspal, & Mayton, 2001; Wiehagen, Mayton, Jaspal, & Turin, 2001). While many industries would require injury records from individual companies or insurance providers to perform an analysis, mining is uniquely suited for a more comprehensive injury analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Each entry of the database contains 36 unique attributes including: mine id, mining method, accident date, degree of injury, accident classification, mining equipment, employee's experience and activity, and a narrative briefly explaining the accident. Previous mining research has examined the injury and fatality causes associated with maintenance and repair, haulage vehicles, ingress and egress from mobile equipment, operating underground and surface mining mobile equipment, and other mining tasks (Drury et al, 2012; Moore et al, 2009; Pollard et al, 2014; Reardon, Heberger, & Dempsey, 2014; Turin et al, 2001; Wiehagen et al, 2001). Traditional injury data analysis uses counts and cross-tabulations as a means to determine trends in injuries.…”

Section: Introductionmentioning

confidence: 99%

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

Dindarloo

Pollard

Siami-Irdemoosa

2016

Journal of Safety Research

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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 5 years 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.

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“…In surface haulage, human performance is a critical issue, as vehicles place high demands on their human operators [1][2][3][4][5]. Many factors contribute to haul truck-related fatal and non-fatal injuries, including lack of visibility, road conditions, operator behaviour, operational conditions and weather conditions [4].…”

Section: Introductionmentioning

confidence: 99%

Causes of Fatal Accidents Involving Coal Hauling Trucks at a Coal Mining Company in Indonesia

Sudiyanto¹,

Hendra²,

Susilowati³

2018

KLS

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Road hauling accidents involving hauling trucks are the highest contributor to coal mine accidents in Indonesia. More than 20 percent of accidents related to coal mining occur on hauling roads. PT X is an open-pit coal mining company that transports coal for processing using hauling trucks. At this company, coal hauling truck accidents also result in the highest number of fatal incidents. During the 2010-2015 period, 11 fatal accidents were reported, and hauling trucks were involved in 45 percent of the accidents. These fatal accidents resulted in the operators' deaths and also caused financial losses because of the subsequent needs to repair equipment and to disrupt production processes. The purpose of the present study was to identify the causes of Meanwhile, the use of mobile phones while driving was related to the monotony of work, the lack of an open radio channel and personal reasons. To reduce fatal accidents involving coal hauling trucks, we suggest to the management of PT X that the work shifts of operators be appropriately managed. A rotating shifts schedule should be implemented, and rest periods should be encouraged during shifts to avoid operator fatigue and decreased operator alertness during day shifts. Finally, we suggest the implementation of further regulations to prohibit the use of mobile phones while driving.

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Patterns in Mining Haul Truck Accidents

Cited by 18 publications

References 10 publications

Development of ergonomics audits for bagging, haul truck and maintenance and repair operations in mining

Development of ergonomics audits for bagging, haul truck and maintenance and repair operations in mining

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

Causes of Fatal Accidents Involving Coal Hauling Trucks at a Coal Mining Company in Indonesia

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