Computer simulations help determine safe vertical boom speeds for roof 

bolting in underground coal mines 

Dean H. Ambrose ,John R.Bwtels, August J. Kwitowski, 

Sean GalIagher, Thomas R. Battenhouse Jr. 

National Imdtrrtafor Ucmpationui Safe& and Health, Pitfsbwh Reseaid I.ubwalo?y Pimbutgh, PA 15236, Unit& Stales 

Problem Incident investigation repwts do not ~lmsllycontain enough information to aid in studying boom arm vertical speed for roof 

bolting machines to determine the impact that appendage speed had on an opmtor's risk of expe+ncing a contact Laboratory expKiments 

with human subjects are also not feasible because of safety and ethical issues. Methad: Researchers successfully developed a threedimensional wmpum model that uses virtual human simulation software as the primary means to gather contact data when the boom arm 

touches the operator's hand, am,head. or leg. Resulk: Data anatysisof roof bolter sirnutations shows that the speed of the boom arm is the 

most important factor in detemrining the risk of an optor making conMct. Regardress orother variabIes, contact incidents were alw~ys 

greater when the bolter arm was moving up, pter on the hand, and greater for the 60Mn arm part of the machine. The mson why the 

subject experimces more conhcts when the boom arm is moving up rather than down is that more risky behaviors occur during drilling and 

bolting when the booin arm is ascending. Based on the data collected, boom speeds grater than 13 idsec result in a substantial increase in 

risk to the roof bolter operator of making contact. Speeds less than or equal to 1 3 inlsec are associated with a more mdest relative risk of 

making contact, which represents a decrease in potential buard. Impacc on Indusiry: The use of such infmation can be quite helpful in 

making recommendationsto machine design and task proeedu~sto reducethe likelihood that roofbolter operatom will experience injury due 

to contact with a moving mf bolting machine's boom am. 

1. Introduction and problem 

Roof bolting is one of the basic functions and most 

dangerous jobs in undergroundcoal mining. Roofbolts are 

the main method of roof support inmines, which is essential 

to ventilation and safety. After miner mews remove a 

section of the coal seam,mf bolting machine (see Fig. 1) 

operators install bolts (steel rods) to secure areas of the 

unsupported mf from caving in. The mf bolter operator is 

under constant production pressure to install as many bolts 

in one $-hourshiR asnecessary to keep up with coal-cutting 

operations while remaining vigilant to all of the possible 

dangers. 

The mof bolter operator does his or herjob in a confined 

environment (see Fig. 21, with a Iimited working height 

(e.g., 45 in.), and in clo~eproximity and in low visibility to 

a moving drill head mounted on a boom arm 72 in. long. 

This restricted work environment can force the operator in 

awkward postures for tasb that require quick reactions to 

avoid contact with moving machine parts. Restricted 

visibility due to a protection canopy and low lighting 

conditions further complicate the task. The Mine Safety and 

Health Administration's @ASHA) Health and Safety Accident Classification injury database showed an average of 

660 roof bolter operator accidents per year over a 5-year 

period (1999-2003). This makes roof bolting a hazardous 
machine-related job in underground mining, rgrcscnting 

39% of all machine-related accidents in underground coal 

mines. 

Fig. I. Roof bolting machine (Courtesy of Fletcher Mining Equipment, 

Cluntingtm, WV). 

FIE. 2. A roof bolter operalor'swork posture In an undcwound coal minc 

workspace environment. 

Protecting the safety of mine workels is of paramount 

impertancc; however, there are currently no regulations or 

methods of determining the safc speed of roof bolter boom 

arms. This articlc reports the initial step to define a safe 

specd range for a roof bolter" boom arm. MSHA accident 

investigation reports do not usually contain scientific 

information to aid in studying interactions between a 

machine and its operator. Tn addition, lab experiments with 

human subjects an: not feasible because of safety and ethical 

issues. With this in mind, NIOSH researchers successfully 

developed a computer model (Fig. 3) that uses UGS PLM 

Solutions' Jack virtual human simulation sofhvarc. The 

model gencratcs data by means of simulation while altering 

several variables associated with the machine and its 

operator. These include coal seam height, the operator's 

an~ropomctty, work posture, choice of risky work behavior, and the machine's appendage velocity. Co-authors 

studied the msulting simulation database to investigate 

appendage speeds and decrease thc number of contacts 

(possiblc injuries) to the miner by improving machine 

designs or operating procedures. 

Fig. 3. A view from the dispray monitor of thc mf bolter mcdcl. 

Expcrfments in other industries have provided some 

evidmcc for resolving safe machine appendage speeds for 

reducing potential hazards. Industries using robots exhibit 

conccrn for guidelines for robotics safety. Etherton (1983) 

reports 10 inls as n speed whereby humans could recognize 

and react to perceived hazard in the system. In addition, 

thc Occupational Safcty and Health Administration 

(OSHA, 1987) reports that robot speeds for teachand-repeat programming sessions are required to be slow. 

The current standard of American National Standards 

Institute (ANSI) recommends that this slow speed should 

not cx~eed 10 inlsec. However, Karwowski, Parsaei, 

Soundararajan, and Pogpatanasuegsa (1992) report that, 

with rcspect to the potential hazards from a moving robot 

arm, test subjccts similarly perceive the range of slow 

speeds of robot motion from 8 to 16 inls. Their study 

suggested that the safe slow spced of robot motions for 

tcaching and programming purposes lics somcwhcre 

between 10 and 8 ids, and for safc reduced specd of 

robot motions redefine the current recommendation of 10 in1 

s. Moreover, thc U.S. Depafirncnt of Energy (1998) states 

that because the teacher can be within the robot's restrictive 

cnvelop, mistakes in programming can result in unintended 

movement, so a restxicted speed of 6 inls is required on any 

part of the robot. This slower speed would minimize 

potential injurics to the tcachcr if inadvcrtcnt action or 

movement occurml. 

This article discusses NIOSH's success in achieving its 

expected outcome TO examine the spmd range of a roof 

bolter boom arm for different workplace scenarios and 

comparc statistically which scenarios arc most likely to 

cause contacts (possible injuries) to mincrs. Previous studies 

by Klishis et al. (1493a 1993b)on worker job performance 

and machinery and work environment identifid mincrs' 

risk and hazard exposures whilc bolting. Morc than two 

dozcn bolting-rclatcd problems (including specific human 
behaviors) were recognized as potential situations that could 

lead to injury or expose workers to Injury. Approaches to 

avoid these situations were suggested and applied at mining 

operations to evaluatt: specific problems in mf bdting 

tasks. Turin (1995) conducted a human factors analysis of 

hazards related to thc movement of the drill hcad on the 

boom arm of a roof bolting machine. Seven short-term 

recommendations to increase the safety of rmf bolting 

operations were developed: (a) use a dead-man interlock 

device to cut offpower to the controls when the operator is 

out of position; (b) pIace fixed barriers at pinch points and 

other dangerous arcas; (c) provide bener control guarding; 

(d) rcducc the fast-fecd speed; (c) use automatic cutoff 

switches for pinch points and other dangerous areas; (f) 

redesign the control bank to conform to accepted ergonomic 

principles; and (g) use resin insertion tools and resin 

cartridge retainers. 

2. Method 

2.1. Model 

The model (Fig. 3) contains a virtual mine environment 

that includes roof bolter and operator models and experimentalIy mimics the virtual human and machine actions 

that can cause a contact. In this article, when operator limbs 

and a roof bolter appendage in the computer modcl interact 

and result in touching, thc cvent is defined as a contact (scc 

Fig. 4). Simulations of the model enable scsearchers to 

generate a database of contacts between a machine and its 

operator. 

Fig. 4. Vittual operator contacted in the left band (or fingers) and left leg. 

NIOSH's simulator uses a roof bolting machine and 

biomechanical human models that execute on Jack (version 

1.2) simulation software. Computer simulations enable the 

study of multiple mine environments (i.e., seams of diffmnt 

heights), motions of workers (represcnted by virtual 

humans), and different work scenarios (e.g., various drilling 

and bolting tasks, work postures, and risky work behaviors). 

Thcse studies would be dangerous and time- and costprohibitive if they werc conducted in thc ficld, 

One of the most difficult problems in using a computer 

simulator that generates human motions is trying to 

determine whether the model in the simulator accurately 

represents the actual mechanical system. The uncertainty 

and randomness inhercnt in machinc operators' tasks can be 

wmparsd to someme drinking a bcverage from a cup. 

Lifting the cup to one's mouth and placing it back onto the 

tabletop exhibits some random variation in its motion path, 

and one could easily visualize the path of that motion. To 

model this random motion, the sequence of someone 

drinking a beveragc from a cup would recur until the cup 

is empty. Each motion path would differ slightly even 

though the motions basically look alike. Likewise, in the 

case of a machine operator, the operator's work behaviors, 

motion for each behavior, and motion paths associated with 

each motion bchavior will have somedcgree of randomness 

despite the basic task sameness. Through careful study, 

researchers successfi~lly incorporated within the roof boltcr 

model the randomness of the operator's motion and path 

variance within that motion. The factor of randomness gives 

N'IOSFI's simulator the capability to mlistically represent 

the operator's motions and work behaviors while exccuting 

any machine task. In addition to mndom motion, the study 

of vidco tapings and captured motions of test subjccts while 

perfotming the bolting task helped to determine the duration 

of each virtual hand motion for the model. This in turn 

improved the affect of the virtual operator's head, arm, hand 

or leg velocity, and acceleration. Arnbrose (2000, 2001, 

2004) and Volbcrg and Ambrose (2002) discuss in detail the 

development of random motions used in the roof bolter 

model. 

Before collecting final simulation data, researchers used 

test results by Bartels, Ambrose, and Wang (2001) and 

Bartels, Kwitowski, and Ambose (2003) on the roof boltcr 

model to validate and ensure that parameter assumptions 

made for the computer-based simulation conform to actual 

field practice. Training videos, in-mine observations and 

videos, and working with a bolter manufacturer and experts 

helped to determine actual boIting practice. Studies by 

Bartels' et al. (2001, 2003) verified the operator's response 

times, task motions, and field of view relative to the roof 

bolter7s boom arm. Human subjcct tests with a full-scale 

working mockup of a boom arm were used to collect motion 

data that helped determine parameters for building valid and 

credible models. The subjects performed prescribed tasks on 

the mockup that mimic bolting ptaclices and did not include 

risky work behaviors as described in this article. Researchers found no differences between test subjects' actual 

bolting practice and recommended practice (according to 

roof bolting training materials). During human subject data 
coIlection, risky work behaviors invalidated a test session, 

resulting in rerunning the test. 

Two different methods to validate the model were 

chosen. The first method was the traditional face validity 

evaluation by roof bolter manufacturers and users. A 

qucstionnaire was developed and distributed to manufacturers, bolter operators, and minc inspectors. The responders 

wcre shown two animations that showed an operatorperforming roof bolting tasks: one was the virtual operator 

produced From the motion-capture data, the other was the 

virtual operator created from the model. The respondents 

werc asked to compare aspects of thc animations without 

knowing which motion sourcc was shown in the animation 

by scoring on a scale from 4 being good to 1 bcing paor. 

The virtual operator produced from the motion-capture data 

scorcd an average of 2.55, virtual operator created Ftom the 

model scorcd an avcragc of 2.34, and the averagc difference 

in qucstionnaire scoring was 0.64. Verification of the 

validity of the model was first implied when 14 of 15 

responders agreed that the simulation animations did not 

dffler significantly from the animations of human operators. 

The second method comparcd the motions generated by 

thc simulation with motion data collmtcd on human 

subjects. Although thc predictions of the model could not 

be directly compared, the accuracy of the movements used 

to generate "contact data" could be. The aspects of operator 

movemcnts detemined to be criticaI were the rangc of 

motion of opcrators and variation in those movements. Thc 

human subject movcmcnt data tended to vary grcatly from 

individual to individual, making it impractical for a direct 

comparison of each individual's exact path of movment. 

Because the amount of movement and the variation of 

movement wcre the primary concerns, the comparisons 

were madc bctween the statistical ranges by using standard 

deviation of movement. 

Two sets of simulation data were generated from motion 

data of the knee and standing work postures. The first used 

virtual operatots with anthropometric measurements identical to thosc of the I2 human subjccts tested, Hcre, the data 

wcrc compared on a subject-to-subject basis. The second set 

used opcrators gencratcd from Jack software in scvcn 

differcnt anthropometric sizes. Researchers compared data 

to an average of the human subjects within a 10th-percentile 

range (e.g., the lack-generated 55th-percentile operator war; 

compared to thc averagc of the subjocts in thc 50th-60th 

pcrccntile range). 

The human subject movmcnt data scnded to vary greatly 

hm individual to individual, making it impractical for a 

direct comparison of each individual's exact path of 

movment. Bccause the amount of movcmcnt and thc 

variation of movement werc the primary conccrns, the 

comparisons werc made betwcen thc statistical ranges by 

using standard deviation of movement. 

Researchers developed two sets of test data to verify the 

model. One se~comparcd Jack-generated operators' motions 

in different work posmres in each of the anthropornetrics 

size ranges with human subject data averaged for that range 

("average" operator; e.g., the Jack-generated 55th-percentile opcrator was comparcd t~ the average of the subjects in 

the 50th-60th-percentile rangc). The other set compared an 

individual zest subject's motions in diffcrcnt work postures 

with a simulation using that subject's anthropometty 

("human subject" operator). The criterion for acccptancc 

of the simulation data was Iess than 1.6-in difference from 

the human subject data, the static positional accuracy of the 

motion-tracking system with the resolution settings used. 

Table 1 shows the percentage of range of motion data in 

differcnt work postures and two operator conditions 

(averagc and human-subjcct) that ma the acccptancc 

criteria. The simulations run using average opcrators 

(generated from Jack software 25th-, 45th-, 55th-, 65th-, 

75th-, 85th- and 92nd-percentile persons) showed a greater 

pcrccntagc that mct thc acccptancc criteria. This would bc 

cxpcctcd sincc avcragcd standard deviation valucs wcre 

used as the input data for the actual simulation. In gcneral, 

the percentage of agreement that met the criteria was very 

good ovemll (70.4%) in relation to modcling work scenarios 

with complexily of roof bolting. 

Table 1 

Data that met the acceptance criteria 

Work wsrure Condition Percent met criteria 

Both knees average-operators 

Both knees human subjcct operators 

Right knee average operators 

Right knee human subject operators 

Standing avcragc operafors 

Standing human si~blectoperators 

Staning position average opton 

Starting position human subject operators 

Overall average 

2.2. Stu~+population 

The study population in the roof bolter model used thrcc 

virtual human models representing operators that conformed 

to the avemgc hcight of 25th-, 55th-, and 92nd-percentile 

males. The three virtual human models were chosen to 

match closcly to human subject data that werc collected for 

model verificationlvalidation and to study the targct 

population, which is 99"h male. 

2.3. Experimental design 

The study evaluated contact data that the computcr 

model generates bctwecn thc machine and its virtual 

operaTor. A recorded contact went occurred when the 

virtual operator and machine interacted and rcsulted in 

touching. Researchers were interested only in contacts 

occurring whcn thc machinc appcndagc was moving. At 

no tirnc during boom am movement was the virtual 

operator positioned in a pinch-point area of the dritl hed 

or boom arm. In addition, a contact does not imply injury, 
although it may pose the potential for the occurrence of an 

injury. 

The modcl used three types of predictor variables: (a) 

fixed variables were used as input for simulation setup, (b) 

conditional variables were randomly selected within the 

computer model and then fixed before executing the 

simulation, and (c) random variables wcre "values" that 

changcd during the simulation exwution. 

The fixcd variables were: 

Roof bolter boom arm speed.-The boom speeds used 

were 7, 10, 13, 16, and 22 idsec from MSHA's Roof 

Bolter-Machine Committee (MSHA, 1994). Whcn thc 

boom arm movd up or down for dnlling or bolting, one 

selected speed was maintaind for all events throughout 

thc simulation execution. 

Seam height.-The area in which the operator had to 

perform the roof bolting procodure is defincd as the 

distance from the floor to the top of the coal seam or roof, 

which may go bqond thc top of the coal seam. The 

specific heights uscd were 45, tiO, and 72 in. 

Operator k posture while performing the roof bolting 

task.-The work postures used were kneeling on the 

right knee, kneeling on the IeR knee, kneeling on both 

knccs, and standing. The one sclectcd work poshlrc was 

maintained throughout the simulation exwution. 

Operator S anthropomesry.-Anthmpometric data often 

are presented in percentiles (Kroerner, 200 1): therefore, 

the operators' percentiles were grouped within the 

gencral population as determined by height. The percentile size operators used in the simulation were identified 

as 25th, 55th, and 92nd. 

The conditional variables werc: 

Operator 5 behavior during the drilling phase of the 

simulation.-Drilling behavior was randomly selectcd 

before beginning the simulation. Thc operator could 

place hisher hand on the drill steel, place hislhcr hand on 

the boom arm, piace hislhcr hand on thc drill steel then 

the boom am,or the hand would not be placed on any of 

the machine parts. A behavior motion is a scrics of 

human motions that mimics a specific action. Researchers used Klishis et al. (1993a, 1993b)studies on worker's 

job performance and machinery and work environmentto 

identify specific motions that were risky and hazardous 

while drilIing and bolting (Table 2). Also, rescarchm 

were interested in behaviors occurring only when the 

machine appendage had movement. 

Qperatork behavior during the bolting phase of the 

simulation.-Bolting behavior was randomly selected 

befm beginning the simulation. The operator could 

place hislhcr hand on thc bolt, placc hisher hand on the 

boom arm, place hisher hand on the bolt then the boom 

arm, or the hand would not bc placed on any of the 

machinc parts. 

Tablc 2 

Behavior list for drilling a hole and installing a bolt 

Operation Work Behavior Description 

Dnll Hand off the drill steel bit and than hand off the boom am 

Hand an the driIl stcel bit 

Hand on the boom arm 

Iland on the driIl steel bit and than hand on the horn arm 

Bolt Hand off the bolt or wrench and than hand off the boom arm 

Hand on the bolt or wrerrch 

Hand on the boom am 

IIand on the bolt or wrench and than on the boom arm 

* Operator .%location.--The operator would be randomly 

positioned with respmt to the bolter at the beginning of 

the simulation. The operator location is defincd as the 

distance from a reference point on the boom arm to a 

reference point inthe small of the operator's back. At no 

time during boom arm movcmcnt was the operator 

positioned in pinch-point amas of thc drill hmd or boom 

arm. 

The random variables were: 

* Boom arm directjon (up or down) .-This is the direction 

in which the boom am was moving whcn an incident, 

either a contact or an avoid incident, occurred. The 

direction could only be one of two directions, up or 

down, and if the boom am was not in motion the 

incident would not be used. 

Body part (hand, arm. leg, and head) .-This is the part 

of the operator involved in an incident. The parts of the 

body that couId potentially be struck by the moving 

boom arm wcrc the hand, arm, Icg, or hcad. 

Machine part eoom arm nnd drilf head).-This is thc 

part of the bolting machine assembly that could strikethe 

operator. The only moving parts used for this simulation 

were thc boom arm and drill head. The drill head is 

attached to one end of thc boom arm. 

As part of the experiment design, the operator's chance 

of avoiding a contact was aIso evaluated to ensure that an 

avoid incident (near-miss) wouId not be considered a 

contact. This rcquircd knowldge of when the operator sws 

the moving boom arm and the rcaction time needd to avoid 

boom arm. Bartels ct aI. (2001) investigation providcd data 

to determine the operator's response times and field of view 

dative to the boom arm. 

The data collection phase took five months to complete. 

The resulting simulation database contains 5,250 observations. The database represent4 the equivalence of actual 

field observations of mf bolting and corresponds to a 

work period of 12.15 eight-hour shifts. Collectcd data 

were rccordod to a file for each simulation scenario 

execution. E'or each file, data were collected every 0.03 
seconds that wnsist of the number of contacts made, the 

time when a contact happens, plus values of predictor 

variables (seam height, machine appendage velocity, and 

the operator's starting positions, work postures, risky work 

behaviors, opcrator's height, and information of hisher 

viewing arca). 

Whcn using the virtual mine environment, separate 

simulations werc executed on each virtual operator while 

performing one of the model scenarios(Table 3). Thirty-five 

scenarios provided thc needed model combinations that 

varied seam heights, work postulcs, and boom arm spds 

to mimic actual mine cnvironments. Virtual human models 

that matched closcly to human subject data wllected for 

model verificationlvalidation were given specific instructions aq to how to perform thc bolting tasks for cach of the 

simulation scenarios. During the boom arm movement, the 

Icft hand's motion would be one of four possible risky work 

behaviom as defined in Table 2. Once the preparation for the 

drilling or bolt installation task waq coinplctd, the right 

hand was positioned on the appropriate lever that controlled 

the boom arm's vertical movement. In each condition, the 

virtual operator was required to work in the starting posture 

throughout the tasks. Thrce knceling postures wcre used in 

the two lower seam heights. The standing posture was usd 

in the unrestricted (high) seam. The standing posturcs for 

the two taller operators flexed more toward the right side 

and forward so to accommodate the workspace and propcr 

right-hand alignment with the machine controls. This 

posturing was also obscrved during lab rests that collected 

human subject motion data for validating thc modcl. Thc 

random starting position between the opesator and boom 

arm were based on seam height and thc operator's work 

posture according to results from human subject lab tests. 

Each virtual operator faccd perpendicular to the long sidc of 

the boom am, and the machine controls wen: always to the 

operator's right. The virtual operator grabbed the tools (drill 

stcel, bolt, or wrench) with the right hand, passed the tool 

off to the left hand, and grabbcd them with both hands to 

finish setting the: tool in the drill head andlor hole in the 

mine ceiling (mine roof). 

Table 3 

Thirty-five possthle model scenarios for each virtual operator 

kenano Seam height Work posture Boom spccd 

(in) (ids) 

45 60 72 RKnee LKncc BKncc Stand 7 EO 13 16 22 

Data from 5,250 simulation execuzions were postprocessed. The resulting database contains information 

representing variables that could influence predictions of 

contact incidents bctwcen the opcrator's body parts and the 

moving boom ann and drill head. The determinations of 

contact incidents for each simulation execution resulted in 

four possible occurrences: 

A contact lbetwecn the machine and the operator for a 

person with both slow and fast rcactions 

A contact betwccn the machine and the operator for a 

persan with just slow reactions 

An avoid incident (near-miss) where a contact occurred 

in the simulation, but post analysis dctcmincd that the 

opcrator saw thc bolter boom arm and had fast enough 

rcactions to get out of the way of (avoid) the contact 

A cornplcte simulation execution where no contacts or 

avoid incidents occurred (nonc) 

A simulation exccuzion would continue until completion 

even though it was possible for a single simulation to have 

multiple contacts and avoids. The presence of multiple 

incidents in a single simulation execution mcant that data 

analysis could tic done on eithcr a data set containing avoids 

and all contacts (all of the contacts) or one incident per 

simulation execution (one rudone contact). Consequently, 

rcscarchcrs madc two separate sets of data from the initiai 

post processed database. Co-authors considered the one run/ 

one contact data set to bc more accurately rcprcscntativc of 

the real-world situation, as an operator would most likely 

stop or at lea~tpause after bcing struck with a moving 

machinc appcndagc. 
3. Results and discussion 

The following section contains results from frequency 

and cross-tabulation, and survival analyses. All analyses 

wcrc conducted using only the occurrences for the opcrator 

with slow reactions that included one contact per simulation 

executions (onc rudonc contact). Analysis also shows that 

the diffcrence betwccn slow and fast reaction times of the 

opcrator did not significantly affmt the outcornc of the 

simulation (Table 4). Thc number of contact incidents for an 

operator with slow reactions differed from those for an 

operator with fast reactions by less than 1% in both data 

sets. The results were as expected insofar as there was a 

difference. There was a reasonable differencc in rcaction 

timcs bctween fast and slow operators obtained from 

reaction time tcsts on our human subjects. Howcver, the 

speculation as to why a small difference in wntach might 

be reflected in the speed range of the boom arm being 

studicd is that if the operator with fast reactions could not 

gct out of the path of the boom am, the slower opcrator 

certainly would not either. In addition, dcpcnding on thc 

stimulus, small diffcrcnccs were found in some reaction 

time test cascs in thc litesaturc search. Moreover, literature 

reviews were not helpful with whole-body reaction of the 

upper torso and limbs in confined spaces, which was a 

concern in our research. 

Tablc 4 

Results of slow mvonsc versus fast for simulation cxccutions 

All contacts One contact pcr simulation 

muenc~ Pcrcmt Cumulative perccnt Frcqucncy Perccnt Curnulat~vepercent 

Slower Operafor 

avoid 2,777 27.02 27.02 755 14.38 14.38 

contact 5,798 56.42 83.45 2,750 52.38 66.76 

none 1,701 16.55 100.00 1,745 33.24 100.00 

Total 10,276 100.00 5,250 100.00 

Fast Optrator 

avoid 2,768 26 94 26.94 799 15.22 15.22 

contac~ 5607 56.5 1 R3.45 2,706 51.54 66.76 

none 1,701 1655 100.00 1,745 33.24 100.00 

Tota1 10,276 100.00 5,250 E 00.00 

3.1. Frequency 

Frequency of incidents was compiled for fixed, conditional, and random variables used in the simulation in order 

to determine their effect on the operator (contacts between 

thc opcrator and thc machine). 

3.1.1. Effects of foced variables: boom orm speed, seam 

height, operator k work posture and anthropornety 

The 60-in scam height had the most contacts, 59% of the 

total number of contacts and 25% of thc avoid incidents. The 

anthropometry did not show a largc diffcrence for any onesize individual, but the 25th-pcrccntilc opcrator had 40% of 

thc total contact incidents. The work posture on both knees 

had the greatest number of contact incidents compared to 

other postures (32% of the total contacts). All boom arm 

speeds resulted in contact incidents; the faster speeds (1 6 

and 22 idsec) accounted for 43% of thc total contacts. 

3.1.2. EEflkcf.cis of conditional variables: operatork work 

behavior and operator S work location 

The hand-on-boom 'behavior for drilling or bolting had 

more contacts than any other drilling or bolting behavior. 

Operator Iocaaion data showed thrce locations with 

increased contact incidents: 21.7,29.9, and 30.3 in. Further 

sorting of opcrator location indicated that thc increase at 

2 1.7 in was associated with Increasd head incidents with 

the opcrator on both hccs in a 60-in seam hcight. Thc 

increase in incidents at 29.9 and 30.3 in were associatcd 

with the operator in a standing position and an increase in 

contacts with the hand. 

3.1.3. Efects of random variahles: horn arm direction, 

body part, and machine part 

Thc boom arm upward dimhon had significantly more 

contacts (76% of the total) and fewer avoid incidcnts (37% 

of thc total) than the down direction. Thc hand was involvcd 

in 67% of all contact incidents, The boom arm was lthe 

closest moving machine part to the operator and account4 

for 80% of all contact incidents. 

A cross-tabulation af incidents was compilcd for selected 

variables used in the simulation in order to dcteminc thcir 

effect on contacts between the operator and rnachinc. Thc 

tabulation of contact incidents by variable showed which 

variables played the largest role in the occurrences of 

potential contacts to operators. 

3.2.I. Eflircts of seam height versus mndom variahles: horn 

arm direction, body part, and machine part 

In comparing scam heights against boom arm direction, 

body part, and machinc part, the following relationships 

were identified. Regardless of seam height, contact incidcnts 
were always greater on the hand, always greater for the 

boom arm part of the machine, and always greater when the 

boom arm was moving up. Thc greatest number of contacts 

was always associated with the 60-in seam. The greatest 

number of contacts occurrcd for the 60-in seam with the 

boom am moving up (46% of all contacts), the 60-in seam 

with contact on the hand (32%ofall contacts), and the 60-in 

seam with contact made with the machinc boom arm (47% 

of all contacts). The fewest number of contacts occurred for 

the 72-in scam with the boom arm moving down and the 45-

in seam with contact made with thc drill head. Zero contacts 

occurred with the operator's leg at a 45-in seam height and 

with the operator's head at a 72-in seam height. 

3.2.2. Efecis of operator anthropometry versus random 

variables: boom arm direction, body part. and machine pars 

In comparing subjects against boom arm direction, body 

part, and machine part, the following rdationships were 

identified. Regardless of subject sizc, contact incidcnts were 

always greater whcn the boom arm was moving up, always 

greater on the hand, and always grcatcr for the boom arm 

part of the machine. The greatest number of contacts was 

always associated with the 25th-percentile size, and the 

fewest numbcr of contacts always occurred with the 92ndpercentile sizc. The greatest number of contacts occvrrcd for 

the 25th-perrsentile size with the boom arm moving up (29% 

of all contacts), occurred on the hand (27% of all contacts), 

and involved the machine boom arm (31% of aII contacts). 

Thc fewcst number of contacts occurred for the 92ndpcrccntile sizc with the boom arm moving down, occurred 

an the arm, and involved the drill hcad. 

3.2.3. Effeecls of operafork work posture versus random 

variahles: boom arm direction, body part, and machine part 

Analysts identified several relationships when comparing 

work posture against boom arm direction, body part, and 

machinc part. Rcgadless of posture, contact incidents were 

always grcatcr when the boom arm was moving up, always 

greater on the hand, and always grcatcr for the boom arm 

part of the machine. The grcatest number of contacts 

occurrcd for the both-knee work posture with the boom 

am moving up (27% of all contacts), lthc right-kncc posture 

with contact made with thc hand (18% of all contacts), and 

the both-knee posture with contact made with the machine 

boom arm (25% of all contacts). The fewest number of 

contacts occurred for the standing posture with the boom 

arm moving down and for the standing posture with contact 

made with the drill head. Zero contacts occurred for the 

cases involving the operator's head in the right-knm, leRknec, and standing work postures and for those involving 

the operator's leg in the both-knee posture. 

3.2.4. Efects of operator 5 drilling behavior versus random 

variables: boom arm direction,bodypart, and machinepart 

Analysts identified several relationships whcn comparing 

dtilIing behavior against boom arm direction, body prt, and 

machine part. Regardless of drilling behavior, contact 

incidcnts were always greater whcn the boom arm was 

moving up, always greater on the hand, and always grcater 

for the boom arm part of the machinc. The greatest number 

of contacts occurred for the handsn-boom behavior with 

the boom arm moving up (42% of all contacts), occurred on 

the hand (41% of all contacts), and involvcd the machine 

boom arm (45% of all contacts). The fewest numbcr of 

contacts occurred for the hand-on-drill-steel behavior with 

the boom arm moving down, handan-drill-steel behavior 

with conhct on the arm, and hand-on-drill-stal behavior 

involving the drill head part of the machine. 

3.2.5. Effects of operator5 bolring behavior versus random 

variables: boom arm direction, body part and machine part 

Analysts identified several relationships when comparing 

bolting behavior against boom arm dircction, body part, and 

machine part. Rcgadless of bolting behavior, contact 

incidents wcre always greater when the boom arm was 

moving up, always greater on the hand, and always grcater 

for the boom arm part of the machine. The greatest numbcr 

of contacts occurred for the hand-on-boom behavior with 

the boom arm moving up (26% of all contacts), occurred on 

the hand (27% of all contacts), and involved the machine 

boom arm (32% of all contacts). The fewest numbcr of 

contacts occurrcd for the hand-on-bolt behavior with the 

boom arm moving down, the hand-on-boom-then-bolt 

behavior with contact on the am, and the handan-bolt 

behavior with contact made with the driIl head. 

3.2.6. Eflects of boom arm speed versus Jxed variables: 

seam height, operator ? work posture and anthropomehy 

For all boom speeds, the work posture on both knees had 

the greatest number of contacts and thc standing posture had 

the fewest number of contacts. The greatest number of 

contacts occurred for the 16-ids% speed with the work 

posture on both knees; thc fewest numbcr of contacts 

occumd for the 22-inlsec spccd while standing. Rcgardlcss 

of boom specd, thc 25th-percentile sizes had the greatest 

numbcr of contacts while, regardless of speed, the 92ndpercentile size had the fewest number of contacts. The 

grcatest number of contacts occurred for thc 13-inlsec speed 

at thc 25th-percentile size. Thc fewcst number of contacts 

occurred for the 10-inlscc speed at the 92nd-perccntile six. 

Regardless of boom speed, the 604n seam height had the 

grcatcst number of contacts. The 72-in seam had the fewest 

number of contacts for all speedsexcept 10 inlscc, wherc the 

45-inseam had the fewcst. The greatest numbcr of contacts 

was associated with thc 16 idscc speed at the 60-in seam 

height. The fewcst number of contacts was for the 10-idsec 

spced at the 45-in seam height. 

3.2.7. Boom arm speed versus conditional variables: 

operator .\ work behavior and operator:r work loca(ion 

RcgardIess of boom speed, the hand-on-boom drilling 

behavior had thc most contacts and, regardless of speed, 
the hand-on-boom bolting behavior had the most contacts. 

Regardless of speed, the hand-on-drill-steel drilling behavior had the fcwcst nnmbcr of contacts and, rcgardlcss of 

speed, the hand-on-bolt bolting behavior had the fewest 

number of contacts. For the drilling behaviors, thc greatest 

numbcr of contacts was for I3 in/scc and hand on the 

boom am;the fcwest number of contacts was for 13 idsec 

and hand on thc drill steel. For thc boIting behaviors, thc 

grcatcst number sfcontacts was for 13 inlsec and hand on 

the boom arm; the fcwest was for 10 in/scc and hand on 

the bolt. 

3.28. Boom arm speed vewu.y random variables: boom arm 

direction, hody part, and mucJiitre par( 

Regardless of boom spd, contact incidents were 

always greater when the boom arm was moving up, 

always grcatcr on the hand, and always greater for thc 

boom am part of thc machine. Thc greatest number of 

contacts occurred at the 16 inJsec specd for the following: 

boom arm moving up (17% of all contacts), hand part of 

the body (16% of all contacts), and the boom arm part of 

the machine (18% of all contacts). The fcwest number of 

contacts occurred for thc 10-in/sec speed with thc boom 

arm moving down, thc 7-inlsec specd involving contact 

with thc arm, and the 22-inlscc speed involving contact 

with the dlill head. 

One of the main interests in performing this survival 

analysis was to dczcrmine thc impact of boom spccd on the 

chance of experiencing a contact in thesc simulations of 

roof 'bolter activities. Results show that boom arm speed 

was thc most influential factor in terms of affecting the 

chance of a contact occurring and the time at which such a 

contact might occur. Morcovcr, results of this analysis 

show that there is a significant increase in thc risk of being 

contactcd at thc two highest boom speeds, 16 and 22 in/ 

scc, comparcd to thc lower speeds (1 3 idsec or less). The 

former were associated with a rnarkcd, and perhaps 

unacceptable, increase in the risk of king contactcd, 

whercas thc risk for thc latter was much morc modest. 

From the current analysis, one can conclude that boom 

speeds abovc 13 inlscc cntail significant chance of being 

contacted. Speeds that arc 13 inlscc or bclow result in a 

much lowcr cxposurc to being contacted, which rcprcsents 

a dmrease in potential hazard. 

Covariates such as operator work behaviom (placing 

the hand on the boom, drill steel, or bolt), work posture 

and seam height combinations. boom am direction, 

operator location, and worker anlthropometry were also 

significant factors in the time-to-evcnt regression analysis. 

Workers wcre morc likely to experience a contact when 

the boom arm was moving in an upward direction, 

especially early in thc roof bolting task. Kneeling work 

postures geneml!y resulted in increased risk of being 

contacted compared to standing in a 72-in seam. KnceEing 

on the right knec within each scam hcight cntailcd the 

grcatcst chancc of a contact. Positioning of the workers 

farther from thc boom arm rcsultcd in a lowcr risk of 

being contacted; however, this could also affect the 

workers' ability to pcrform the roof bolting task, Larger 

workers wcrc 25% more likely to make contact with the 

boom am, whcreas smaller workers were about 5% less 

likely to makc contact. Drilling behaviors such as placing 

the hand on the boom arm or drill steel resulted in a 

greater chance of a contact, while bolting behaviors 

(occurring latcr in thc bolting cyclc) increased the time 

whcn thc event occurred. 

It should be noted that this suwival analysis was 

developed using a main effccts model only. It is posdblc 

that the factors examined in this rcport have inltcractive 

effects (for instance, boom spced could havc morc of an 

impact on thc chance of bcing contacted when certain work 

postures are adopted). The large number of simulations, 

computational demands of running Cox regression models 

and of checking proporrional hazard assumptions, and the 

largc numbcr of inktactions (120) made analysis of these 

interactions impractical given the time constraints involved, 

Ambrose ct al. (2005) describes in detail the technical 

information regarding the methods and results of the 

survival analysis for those interested in the technical aspects 

of the analysis. 

4. Summary 

NIOSH rcscarchcrs suc~ssfully developed a wmputcr 

model that generates contact data by means of simulation 

while exercising the model with several variablcs 

associated with thc machine and its operator, such as 

coal scam height, the operator's anthropomctry, work 

posturc and choice of risky behavior, and thc machine's 

appendage velocity. The resulting simulation database 

contains 5,250 observations. The databasc rcprcsented the 

equivalence of actual ficld observations of roof bolting 

and corresponds to a work period of 12.15 eight-hour 

shifts. 

Analysts used data only on the occurrences for the 

operator with slow reactions that included one incidcnt pcr 

simulation exccution (one runlone contact). Co-authors 

believe the use of such simulations, treated with statistical 

proccdurcs such as frequency, cross-tabulation, and survival 

analysis provide extremely use&] tools to evaluate the 

hazards of tasks whcrc it is not possible to pcrform 

experiments with human subjects. 

Significant results from frequency distribution analyses 

showed: 

* The seam hcigha of AO-in gavc the most contacts-59% 

of the total number of contacts and scam height 45-in 

gave 75% of the near misses. 
Anthropometry did show that the 25th-percentire individual had 7% mom contacts than the 55th-percentile and 

13% more then the 92nd-pmcntilc. 

Operators*work posture indicated that a posture on one 

hcc accounted for 44% of the contacts and a work 

posture on both knees resulted in 32%. 

The spood of the boom arm had thc grcatcst effect on thc 

number of contacts for the fastcr two boom speeds, 43%, 

for 16 and 22 ids. 

The hand on boom work behavior for both dnlling and 

bolting tasks accounted for the majority of contacts. 

The boom arrn up direction had most contacts-76% of 

the total number of contacts and boom arm down had 

63% of the avoid incidents. 

The hand is the closest body part to thc moving boom 

am and was associated with 67% of all contacts and the 

lcg came in second at 15%. 

The boom arrn would be the closest moving machine part 

to the operator and the boom arm accounted for 80% of 

all contacts. 

Regardless of other variables, contact incidents were 

always grcater when the bolter am was moving up, wcrc 

always greatcr on the hand, and were always greater for 

the boom arm part of the machine. The reason why the 

subjcct cxpcrienccs more contacts when the boom arm is 

moving up rather than moving down is due to more risky 

behaviors occumng during drilling and bolting whcn 

boom am is ascending. 

Significant results regarding boom speed from crosstabulation analyses showcd: 

Regardless of boom speed, boom arm up direction 

experienced more contacts than did boom arm down. 

* In addition, the boom arm up direction had most of its 

contacts in the two higher spesds, 22% during speed 16 

ids and 2 1 % during specd 22 ids. 

* Rcgardlcss of specd, the operator's hand cxperienccd 

morc contacts than did other body part. 

In addition, thc hand had most of its contacts; 21% 

during speed 13 ids and 24% during speed 16 ids. 

Thc boom arm cxpericnced morc contacts than did other 

machinc part and had most (22%) of thcir contacts during 

both speeds 16 and 22 ids. 

Both knee work posture experienced morc contacts rhan 

did othcr postures and had most (23%) of their contacts 

during specd 16 ids. 

= The 25th-percentile operators experienced more contacts 

than did other operator sizes and had most (22%) of their 

contacts during spced 13 ids. 

* Rcgardless of speed, thc hand on boom work behavior 

during drilling and bolting tasks experienced more 

contacts than did other behaviors. 

In addition, drilling task had most (24%)of their contacts 

during speed 13 ids and bolting had most (22%) OF their 

contacts during the samc speed. 

The 60-in seam cxperienced more contacts than did other 

scam heights and had most (22%) of the contacts during 

speed 16 ids. 

Results of a survival analytic approach: 

Results suggested that controlling the boom specd is the 

most important factor in determining the chance of an 

optor making contact. 

-

Also, boom spced was thc most influential variable for 

explaining the time to an cvent (contact) occursing.

Increases in boom speed resulted in increased chance of a 

contact throughout the period of the simulation. 

The chance of being contacted at the higher speeds, 16 

and 22 ids, was generally 2 to 4 times grcater than at 13 

ids, and 4 to 8 tirncs greater than at I0 ink. 

Based on the data collected in this simulation analysis, a 

boom arm speed grcater than 16 ids rcsulted in a 

substantial incrcase in the chance of the boom arm 

making contact with the roof boltcr operator. 

* In addition, rcsults showed that speeds less than or 

qua1 to 13 ids resulted in a smaller chance of being 

contacted, which represents a decrease in potcntial 

hazard. 

Acknowledgments 

The authors gratefully acknowledge the following 

colleagues at thc NIOSH Pittsburgh Rcscarch Laboratory: 

Joscph P. DuCamc, Mary Ellen Nclson, Albert H. Cook, 

George F. Fischer, Albert L. Brautigam providcd technical 

expertise in the design and fabrication of the roof bolter 

mockup and platform, and the associated electrical and 

hydraulic control systems. E. William Rossi providcd 

technical support in operating the motion tracking and data 

collection system. Mary Ellen Nelson, Albm H. Cook, and 

May Ann Rossi assisted in the lab human subject tests. 

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