Patterns in Mining Haul Truck Accidents

Colin G. Drury, Applied Ergonomics Group Inc.

William L. Porter, National Institute for Occupational Safety and Health

Patrick G. Dempsey, National Institute for Occupational Safety and Health

To help develop ergonomics audit programs for mining, one source of data on both work tasks and 

their failures is accident reports. These are available in most industries and are often used in human 

factors engineering, but typically to justify and evaluate interventions rather than to provide task 

details and failure mechanisms. Because fatal accidents in particular contain considerable detail 

resulting from thorough follow-up investigations, they are thus a useful starting point for analysis. A

set of 40 detailed fatal accident reports for mining haul truck accidents were analyzed to develop 

repeating patterns of accidents. This paper examines the accident patterns found from the initial

sample, the refined pattern analysis developed from a subsequent larger sample, and the task analyses 

used later to help develop valid audit programs.

INTRODUCTION

Mining accidents and injuries can be serious, even 

fatal, and occur frequently enough to be of constant 

concern to the industry, the workforce, and the regulatory 

authorities. In the U.S. alone, the average number of

mining-related fatal accidents in 2009/2010/2011 was 47 

per year, while in 2011 there were also 7,494 reported

mining-related injuries (US Department of Labor, 2011). 

Santos et al. (2010) analyzed accident/injury reports for one 

type of mishap—haul truck use. Haul trucks are a heavy 

type of earth-moving equipment used in mining operations, 

and commonly carry 35-400 t of payload, with cabs 3-5 m 

above ground level. With such sizes of equipment, the 

marshaling of energy in use can be extreme, with 

consequent serious injury potential. As Santos et al. (2010) 

report, most of the injuries result from accident patterns 

commonly studied by the human factors/ergonomics (HFE) 

community: slips, falls, and struck against. The Santos et 

al. analysis of 1,382 injury records from five years of the 

Mine Safety and Health Administration (MSHA) database 

showed frequent HFE causal factors, including

ingress/egress, maintenance, foot slippage, and equipment 

failure (see also Moore et al., 2009).

As part of a larger project to develop an HFE audit 

program (c.f., Drury and Dempsey, 2012) for use in surface

mining, the research team involved in the current study 

needed to understand the tasks that operators perform and 

how these tasks can fail. This would provide the basis for 

asking pertinent questions in the audit (see Dempsey et al., 

2012). One source of data on both work tasks and their 

failures is accident reports. These are available in most 

industries and are often used in HFE, but typically to justify 

and evaluate interventions rather than to provide task 

details and failure mechanisms. Fatal accidents contain 

considerable detail resulting from the thorough 

investigations required at this ultimate level of severity, and 

are thus a useful (if sobering) starting point for analysis. 

The detail contained in the MSHA “fatalgrams” and fatal 

accident reports 

(http://www.msha.gov/FATALS/FAB.HTM) provided a 

useful basis to start this work. MSHA fatalgrams include a 

description of the accident, details of the investigation, a 

discussion of causes, a root cause analysis, and 

enforcement actions. They are prepared by professional 

safety investigators. However, these reports do not attempt 

to classify the accident using the MSHA non-fatal accident 

classification systems. This last fact prevented the type of 

analysis performed by Santos et al. (2010), but also freed 

the investigators to develop “bottom-up” scenarios which 

reflected the overall pattern of each accident. Such a system 

has been used in the past by Drury and Brill (1983) for 

consumer products and by Wenner and Drury (2000) for 

aircraft ground damage accidents. Typically, strong 

repeating patterns of accidents emerge and remain stable as 

more accidents are analyzed. 

FATAL ACCIDENT PATTERNS

A set of 40 detailed fatal accident reports for haul truck 

accidents were analyzed to develop repeating patterns of 

accidents (c.f. Wenner and Drury, 2000). These reports 

comprised 20 coal and 20 metal/non-metal haul truck 

fatalgrams, with the two different types of operations 

having potentially different underlying tasks and patterns

and different regulatory requirements. These reports were 

read through and potentially revealing words, phrases, and

sentences were marked for comparison. Several iterations

Not subject to U.S. copyright restrictions. DOI 10.1177/1071181312561420

PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 56th ANNUAL MEETING - 2012 2011
Figure 1: Initial Patterns for haul truck accidents.

of a coding scheme were used to produce a hierarchical 

scheme so that in the future the patterns could be collapsed 

upwards as needed—for example in formal tests of unequal 

distribution of causal factors across accident patterns (c.f. 

Wenner and Drury, 2000). Figure 1 shows this hierarchical 

set of accident patterns.

Note that for development of accident patterns, a 

relatively small sample is all that is required, because these 

are repeating patterns by definition. Note also that at this 

stage there is no attempt to count the relative frequencies, 

with the issue being one of finding repetitive patterns rather 

than guiding policies for safety improvement. Once 

adequate (see below) and stable (see above) patterns are 

determined, their definitions can be used for a more formal 

classification of larger databases, such as all fatalities over 

several years, or even non-fatal accidents. The adequacy of 

the derived patterns is measured by the number of accidents 

unable to be classified—the “Other” category. For this 

initial review of 40 reports, none were found to be 

unclassifiable.

In Figure 1 the accident patterns are described in a few 

words, but we can define them more completely as follows.

For all of the accidents analyzed here, the outcome was a 

fatality, so that only the precipitating event is detailed in 

these descriptions, not the full accident event. A more 

refined set of the lowest level accident patterns will be 

presented in later figures.

1. Driving: The haul truck is being driven at the time of 

the accident.

1.1. Loss of Control: The driver experiences a loss of 

the ability to control the vehicle, either through a vehicle 

failure or error in following the intended course.

1.2. Ground Fails: The ground on the intended course 

gives way under the weight of the haul truck.

1.3. Two-vehicle Collision: Two vehicles collide, with 

either or both drivers contributing to the accident.

2. Non-Driving: The haul truck is not being driven at 

the time of the accident.

2.1. Unexpected Movement: While the operator is not 

expecting movement, the vehicle itself moves over the 

ground or part of the vehicle or its load moves relative to 

the vehicle.

2.2. Falls from Vehicle: The operator falls when 

moving on the vehicle, either during ingress/egress or 

vehicle maintenance.

2.3. Hit by Other Vehicle: The operator has left the 

vehicle and is hit unexpectedly by another vehicle.

The incidence of accidents in each of these accident 

patterns can be counted and tested to determine whether the 

pattern changes with external factors. As part of this study, 

a set of 133 fatality reports (107 haul truck operator and 26 

non-haul truck operator) representing all haul truck-related 

fatal accidents for 1995 through 2010 were classified based 

on the related accident reports to determine the relevant 

pattern. The first outcome of this analysis was that there 

were no accidents among these 133 that could not be 

classified according to the patterns shown in Figure 1 at the 

highest aggregated level (i.e. Driving and Non-Driving). 

Further, the distribution of driving/non-driving accident 

frequencies changed significantly between haul truck 

operators and non-haul truck operators (chi-square (1) = 

PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 56th ANNUAL MEETING - 2012 2012
18.1, p < 0.001). Further breakdown by the separate 

accident sub-patterns (1.1, 1.2, etc.) was not analyzed 

because the chi-square test had three cells with expected 

values < 5. For operators, 75% of the accidents occurred 

during the driving pattern, while for non-operators, only 

31% were while driving. 

Refining the Classification

Subsequent to the above analysis, a research team 

member attempted to classify all fatal haul truck operator

accidents from 1995 to 2010 (n=107). The main patterns 

were found to be adequate, but at the lowest level (1.1. 

Loss of Control and 2.1. Unexpected Movement) more 

detail could be used to classify the patterns more finely. 

These more detailed classifications are given in Figures 2, 

3, and 4 to show how a bottom-up analysis can be refined 

with continued use and additional data.

At this more detailed level, causal factors begin to 

emerge, e.g., Speed (1.1.2.1.) or Alcohol/drugs (1.1.2.5) in 

Figure 3. In addition, the main systems whose failure 

precipitates the accident are more clearly delineated, e.g.,

1.1.1.2 Steering Failure or 1.1.1.5.5 Service Brakes Fail in 

Figure 2. Failure of procedures can be seen in 2.1.3 Driver 

Leaves Cab Without Brakes, particularly the first two subclasses (2.1.3.1 and 2.1.3.2) in Figure 4. 

Figure 2: Further expansion of 1.1.1.

TASK DESCRIPTION DERIVATION

The above classification analysis leads to the 

determination of failures within the 

Task/Operator/Machine/Environment system, i.e. system 

failure modes. Implied in the analysis is also the set of tasks 

performed by the operators that have led to failures. 

Accident analysis is one traditional way of locating failure 

modes in human/machine systems. A complementary way 

is using task description and task analysis. While accident 

analysis finds existing failures (Reason, 1990), task 

analysis can find potential failures that do not necessarily 

appear in current accident records (Bisantz and Drury, 

2005). Task analysis typically begins with a task 

description, using a scheme such as Hierarchical Task 

Analysis (HTA) (e.g., as presented by Stanton, 2006). This 

can involve either derivation of system tasks and structure 

from system descriptions and objectives, or observation of 

the current system if it exists (Drury, 2008). HTA gives 

hazard patterns and their logical precursors not yet seen, 

and is

Figure 3. Further expansion of 1.1.2.

therefore best suited to rare events and new or changed 

systems. In contrast, accident analysis gives rich detail of 

events that have already happened, making it well suited to

long-running systems and deriving detailed precursors. 

However, accident analysis can only prevent the repeating 

of accidents that have already occurred, and relies on the 

quality of accident investigations and the resulting reports. 

Clearly, both HTA and accident analysis are needed to 

derive a comprehensive set of valid accident precursors for 

eventual inclusion into an audit system.

PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 56th ANNUAL MEETING - 2012 2013
Figure 4: Further expansion of 2.1.

For this paper, the research team carried out 

observational studies of the work of driving haul trucks

(see Dempsey et al., 2012), but guidance could be provided 

with an HTA developed from the activities recorded in the 

fatal accident reports already studied and classified. The 

highest level of the HTA really requires nothing beyond 

knowledge of the logic of the operation: Load, Drive, 

Unload, and Return Unloaded. From the injury analyses we 

can add one high-level task—Maintenance. The actual 

HTA derived from accident analyses included two levels of 

lower-level re-description. In fact, the HTA for 

Maintenance was imported directly from a generic 

maintenance task description developed earlier for aircraft 

maintenance activities (Drury, 2009), but was of sufficient 

generality to apply to any maintenance tasks.

Figure 5 shows the HTA of “Operate Haul Truck”

derived from the task descriptions implicit in the accident 

reports. Its main use here was as guidance for more detailed 

task description activities based on direct observation of the 

tasks themselves (Drury and Dempsey, 2012). As a partial 

check on a task different from haul truck maintenance, the 

maintenance HTA (“5 Maintain Truck” in Figure 5) was 

matched with an existing HTA of a different mining task

(changing a conveyor motor). These two HTAs were 

compared and many similarities were seen, suggesting that 

the generic task description can be used as an overall 

structure for HTAs of maintenance activities, whether of 

haul trucks or other equipment in mines.

Using accident reports as the basis of initial HTAs 

raises the question of what should be recorded in a task 

description. Accidents are by definition rare events and the 

sequence of tasks may not be representative of tasks 

typically performed by the operator, as these “normal” 

Figure 5: Initial HTA of “Operate Haul Truck”

tasks did not in fact lead to an accident. The issue of what 

to observe for a task analysis has been covered at length in 

prior works, see for example Drury (2008). The task 

analysis can describe what tasks people are required to 

perform in a system (e.g. as listed in technical or training 

manuals), or even be part of the systems design process for 

new systems. For existing systems, one can observe what 

tasks workers actually perform rather than what the system 

designers thought they would do. As Drury (2008) points 

out: 

“It would be a rather lazy HFE practitioner who 

merely observed the current system. There will always 

be a tension between using what should be done to 

guide the TA and using what is done, as they may 

differ appreciably.”

Haul truck accident reports certainly detail what tasks 

were performed, at least as far as the investigators can 

determine in a fatal accident. However, it is of interest to 

ergonomists to determine why the performed tasks differed 

from what the designers intended should be done, as that 

can reveal for example socio-technical system pressures on 

the operator. An example would be the pressure for on-time 

completion of tasks. As with the rest of this project, such 

PROCEEDINGS of the HUMAN FACTORS and ERGONOMICS SOCIETY 56th ANNUAL MEETING - 2012 2014
analyses can suggest appropriate questions for an 

ergonomics audit program.

IMPLICATIONS FOR ERGONOMICS AUDIT

Classifying accidents into repeating patterns helps to 

reduce the sheer volume of data to be considered in 

progressing towards a valid ergonomics audit program, as it 

reduces potentially hundreds of individual accident reports 

to a set of less than 20 patterns. Also, because these 

patterns are derived in a bottom-up manner from detailed 

reading of the rich descriptions in the original reports, there 

is no requirement to fit each aspect of each report into a 

category that may not apply. This differs from the more 

typically used ANSI Z16-based injury recording systems, 

such as the US Department of Labor’s Occupational Injury 

and Illness Classification system (OIICS) (see U.S. 

Department of Labor Bureau of Labor Statistics, 2011). In 

that system, very generic classifications are used for the 

ergonomically interesting source of injury and event or 

exposure, whereas very specific classifications are used for 

the nature of injury and body part affected. Developing 

specific accident patterns for an occupation or task may not 

be as easy as using a standard classification scheme, but is 

potentially more revealing in terms of causal factors to be 

eliminated to improve safety.

While accident analyses provide causal factors of high 

face validity (e.g. “berm collapsed when haul truck drove 

up to it”) that can be used directly in audits, they also 

provide more general factors derived from task analyses. 

Here we have only presented the task descriptions, but an 

HFE uses these descriptions with human performance data 

to determine potential mismatches between task demands 

and human capabilities (e.g. Stanton, 2006; Drury, 2008). 

As part of an overall project reported in Dempsey and 

Drury (2012), these task analyses are being derived from 

the initial task descriptions presented here. However, a 

greater depth of re-description is required to locate these 

mismatches and re-purpose them into audit items.

Finally, a use of these accident analyses that has only 

been hinted at in this paper is the counting of 

incidents/accidents/fatal accidents to make the case for 

eventual intervention aimed at reducing incident frequency 

or outcome severity. Once the HFE analysis has identified 

causal factors in actual accidents, the counting of these 

accidents becomes a powerful way to justify interventions. 

For example, the various mechanical failures of haul trucks 

(Figure 2) suggest that better design and/or maintenance of 

equipment would lead to a reduction of accidents due to 

these causes. Using the frequencies of the patterns derived 

here reveals how many accidents could be prevented by 

different degrees of effectiveness of design/maintenance. 

By starting from the most severe accidents, i.e. those 

causing fatalities, the case for intervention becomes very 

strong on both cost and social grounds.

DISCLAIMER

The findings and conclusions in this report are those of 

the authors and do not necessarily represent the views of 

the National Institute for Occupational Safety and Health.

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