Human Health and Safety Risks Management in Underground Coal Mines Using Fuzzy TOPSIS

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Human health and safety risks management in underground coal mines using fuzzy TOPSIS Satar Mahdevari a, , Kourosh Shahriar a , Akbar Esfahanipour b a Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran b Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran HIGHLIGHTS Risks associated with health and safety of coal miners were investigated. A reliable methodology based on Fuzzy TOPSIS was developed to manage the risks. Three underground mines in Kerman coal deposit were selected as case studies. The model can help in taking appropriate measures before accidents can occur. abstract article info Article history: Received 17 February 2014 Received in revised form 19 April 2014 Accepted 20 April 2014 Available online xxxx Editor: Adrian Covaci Keywords: Risk management Human health and safety Fuzzy TOPSIS Kerman coal deposit Underground mining The scrutiny of health and safety of personnel working in underground coal mines is heightened because of fatal- ities and disasters that occur every year worldwide. A methodology based on fuzzy TOPSIS was proposed to assess the risks associated with human health in order to manage control measures and support decision- making, which could provide the right balance between different concerns, such as safety and costs. For this purpose, information collected from three hazardous coal mines namely Hashouni, Hojedk and Babnizu located at the Kerman coal deposit, Iran, were used to manage the risks affecting the health and safety of their miners. Altogether 86 hazards were identied and classied under eight categories: geomechanical, geochemical, electrical, mechanical, chemical, environmental, personal, and social, cultural and managerial risks. Overcoming the uncertainty of qualitative data, the ranking process is accomplished by fuzzy TOPSIS. After running the model, twelve groups with different risks were obtained. Located in the rst group, the most important risks with the highest negative effects are: materials falling, catastrophic failure, instability of coalface and immediate roof, redamp explosion, gas emission, misre, stopping of ventilation system, wagon separation at inclines, asphyxiation, inadequate training and poor site management system. According to the results, the proposed methodology can be a reliable technique for management of the minatory hazards and coping with uncertainties affecting the health and safety of miners when performance ratings are imprecise. The proposed model can be primarily designed to identify potential hazards and help in taking appropriate measures to minimize or remove the risks before accidents can occur. © 2014 Elsevier B.V. All rights reserved. 1. Introduction With regard to different occurrences that may lead to fatal or non- fatal injuries, underground coal mining has been recognized as one of the riskiest operations worldwide (Lama and Bodziony, 1998; Sari et al., 2004, 2009; Duzgun and Einstein, 2004; Joy, 2004; Duzgun, 2005; Grayson et al., 2009; Maiti and Khanzode, 2009; Paul, 2009; Shahriar and Bakhtavar, 2009; Zhu and Xiao-ping, 2009; Khanzode et al., 2011). Generally, work conditions in underground mining are dif- ferent from surface mining. Special equipments in the stopes, working at depth, water/mud inrush, gas release, humidity, air pollution and necessity of ventilation system, various illnesses, e.g. pneumoconiosis and severe emphysema, mortality, accidents during loading, hauling or hoisting, stability analysis and strata control, spontaneous combus- tion, outbursts, explosions, etc. are some of the issues that increase the risks of working underground. These difculties are commonly faced problems of underground coal mines, which may have detrimental effects on workers in the form of injury, disability or fatality as well as on mining companies due to downtimes, interruptions in the operations, equipment breakdowns and so on. However, the coal mining industry has made tremendous improve- ments in reducing the fatality and injury rates (Kinilakodi and Grayson, Science of the Total Environment 488489 (2014) 8599 Corresponding author. Tel.: +98 21 64542972; fax: +98 21 66405846. E-mail address: [email protected] (S. Mahdevari). http://dx.doi.org/10.1016/j.scitotenv.2014.04.076 0048-9697/© 2014 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

description

The scrutiny of health and safety of personnel working in underground coal mines is heightened because of fatalitiesand disasters that occur every year worldwide. A methodology based on fuzzy TOPSIS was proposed toassess the risks associated with human health in order to manage control measures and support decisionmaking,which could provide the right balance between different concerns, such as safety and costs. For thispurpose, information collected from three hazardous coal mines namely Hashouni, Hojedk and Babnizu locatedat the Kerman coal deposit, Iran, were used to manage the risks affecting the health and safety of their miners.Altogether 86 hazards were identified and classified under eight categories: geomechanical, geochemical,electrical, mechanical, chemical, environmental, personal, and social, cultural and managerial risks. Overcomingthe uncertainty of qualitative data, the ranking process is accomplished by fuzzy TOPSIS. After running themodel, twelve groups with different risks were obtained. Located in the first group, the most important riskswith the highest negative effects are: materials falling, catastrophic failure, instability of coalface and immediateroof, firedamp explosion, gas emission, misfire, stopping of ventilation system, wagon separation at inclines,asphyxiation, inadequate training and poor site management system. According to the results, the proposedmethodology can be a reliable technique for management of the minatory hazards and coping with uncertaintiesaffecting the health and safety of miners when performance ratings are imprecise. The proposed model can beprimarily designed to identify potential hazards and help in taking appropriate measures to minimize or removethe risks before accidents can occur.

Transcript of Human Health and Safety Risks Management in Underground Coal Mines Using Fuzzy TOPSIS

Page 1: Human Health and Safety Risks Management in Underground Coal Mines Using Fuzzy TOPSIS

Science of the Total Environment 488–489 (2014) 85–99

Contents lists available at ScienceDirect

Science of the Total Environment

j ourna l homepage: www.e lsev ie r .com/ locate /sc i totenv

Human health and safety risks management in underground coal minesusing fuzzy TOPSIS

Satar Mahdevari a,⁎, Kourosh Shahriar a, Akbar Esfahanipour b

a Department of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iranb Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran

H I G H L I G H T S

• Risks associated with health and safety of coal miners were investigated.• A reliable methodology based on Fuzzy TOPSIS was developed to manage the risks.• Three underground mines in Kerman coal deposit were selected as case studies.• The model can help in taking appropriate measures before accidents can occur.

⁎ Corresponding author. Tel.: +98 21 64542972; fax: +E-mail address: [email protected] (S. Mahdev

http://dx.doi.org/10.1016/j.scitotenv.2014.04.0760048-9697/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 17 February 2014Received in revised form 19 April 2014Accepted 20 April 2014Available online xxxx

Editor: Adrian Covaci

Keywords:Risk managementHuman health and safetyFuzzy TOPSISKerman coal depositUnderground mining

The scrutiny of health and safety of personnelworking in underground coalmines is heightened because of fatal-ities and disasters that occur every year worldwide. A methodology based on fuzzy TOPSIS was proposed toassess the risks associated with human health in order to manage control measures and support decision-making, which could provide the right balance between different concerns, such as safety and costs. For thispurpose, information collected from three hazardous coal mines namely Hashouni, Hojedk and Babnizu locatedat the Kerman coal deposit, Iran, were used to manage the risks affecting the health and safety of their miners.Altogether 86 hazards were identified and classified under eight categories: geomechanical, geochemical,electrical, mechanical, chemical, environmental, personal, and social, cultural and managerial risks. Overcomingthe uncertainty of qualitative data, the ranking process is accomplished by fuzzy TOPSIS. After running themodel, twelve groups with different risks were obtained. Located in the first group, the most important riskswith the highest negative effects are: materials falling, catastrophic failure, instability of coalface and immediateroof, firedamp explosion, gas emission, misfire, stopping of ventilation system, wagon separation at inclines,asphyxiation, inadequate training and poor site management system. According to the results, the proposedmethodology can be a reliable technique for management of the minatory hazards and coping with uncertaintiesaffecting the health and safety of miners when performance ratings are imprecise. The proposed model can beprimarily designed to identify potential hazards and help in taking appropriate measures to minimize or removethe risks before accidents can occur.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

With regard to different occurrences that may lead to fatal or non-fatal injuries, underground coal mining has been recognized as one ofthe riskiest operations worldwide (Lama and Bodziony, 1998; Sariet al., 2004, 2009; Duzgun and Einstein, 2004; Joy, 2004; Duzgun,2005; Grayson et al., 2009; Maiti and Khanzode, 2009; Paul, 2009;Shahriar and Bakhtavar, 2009; Zhu and Xiao-ping, 2009; Khanzodeet al., 2011). Generally, work conditions in undergroundmining are dif-ferent from surface mining. Special equipments in the stopes, working

98 21 66405846.ari).

at depth, water/mud inrush, gas release, humidity, air pollution andnecessity of ventilation system, various illnesses, e.g. pneumoconiosisand severe emphysema, mortality, accidents during loading, haulingor hoisting, stability analysis and strata control, spontaneous combus-tion, outbursts, explosions, etc. are some of the issues that increase therisks of working underground.

These difficulties are commonly faced problems of underground coalmines, which may have detrimental effects on workers in the form ofinjury, disability or fatality as well as on mining companies due todowntimes, interruptions in the operations, equipment breakdownsand so on.

However, the coal mining industry has made tremendous improve-ments in reducing the fatality and injury rates (Kinilakodi and Grayson,

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86 S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

2011), the disasters are posing new challenges regarding thewayman-agers assess and mitigate high-risk conditions.

Coal mining has many hazards that make it unique in the field of in-dustrial health and safety. The hazardous nature of coalmine operationscan easily be deduced from the national statistics of mine accidents andinjuries (Paul, 2009). Based on Bureau of Labor Statistics (2010) under-ground coal mining is a relatively dangerous industry and employeesare more likely to be fatal or to incur a non-fatal injury or illness, andtheir injuries are more likely to be severe than workers in privateindustry as a whole.

It is true that underground coalmining is inherentlymore hazardousthan other industries, but this does notmeanwe should simply accept itto be more dangerous. Allanson (2002) after investigating variousaccidents demonstrated that inmost cases sufficient information shouldbe collected to prevent an accident prior to taking place.

One of the main causes of deaths or injuries is working at stopeswhich are considered unsafe as they have an unsupported roof.Working with or nearby underground coal mining equipments is alsoinherently hazardous due to the several sources of injuries and adverseenvironmental conditions. The underground environment in a miningoperation is constrained by the absence of natural light, fresh air, openspace, etc., andmoreover there is the undesirable presence of high tem-perature, humidity, dust, fumes, mist, noise, rock stresses, etc. In addi-tion, working conditions in underground mining are associated with aconsiderable number of health risk factors, such as high physical work-load, noise pollution, vibration syndrome, radiation exposure, diesel ex-haust, and exposure to coal and silica dusts or harmful gases. Due tothese constraints, the potential hazards associated with undergroundcoal mines may trigger accidents unless risk measures are taken to pre-vent them.

The Australian Mine Health and Safety Regulation (2010) requiresmines to conduct risk assessments in relation to certain high risk andhazards associated with ground instability, inrush, atmosphericcontamination, excavation of mine shafts and galleries, slipping ofconveyors, earth moving machinery, fire, explosives and electricalworks. Besides the main reason for conducting a risk analysis is tosupport decision-making, so that it can provide an important basis forfinding the right balance between different concerns, such as safetyand costs (Aven, 2008).

Typically, a risk assessment starts with hazard identification(Göransson et al., 2014). According to the international organizationfor standardization (ISO Guide, 2009) a hazard is defined as a sourceof potential harm,which can be a risk source. According to the definitionof the project management body of knowledge (PMBOK, 2008), risk isan uncertain event or condition that, if it occurs, has positive or negativeeffects on at least one of the nine project objectives, such as integration,scope, time, cost, quality, human resources, communications, risk andprocurement.

Risk is measured in terms of likelihood and consequence, and isdefined as the chance of something happening that will have an impactupon objectives (Barnes, 2009). In this research, risk is the chance ofsomething happening in underground coal mines that will havenegative impacts on the health or safety of a miner.

Risk management is defined as all measures and activities carriedout to manage risk. In other words, risk management deals withbalancing the conflicts associated with exploring opportunities on theone hand and avoiding losses, accidents and disasters on the otherhand (Aven and Vinnem, 2007). Risk management aims to reduce thelikelihood and impact of mishaps of all kinds. In the mining industry,with its inherent potential for major accidents which could lead toinjury, fatality, damage the environment and cause serious loss ofproduction and profit, there is a particular need for a safe and soundapproach to the process of risk management.

Risk assessment as the central part of risk management, is theprocess used to determine risk management priorities by evaluatingand comparing the level of risk against predetermined standards, target

risk levels or other criteria. Therefore, risk assessment involves adetailed and systematic examination of any activity, location or opera-tional system to identify hazards.

There are numerous risk assessment techniques available, each ofwhich may be useful in particular circumstances. While there is nosingle method which is the correct one for any particular situation,engineers should consider the special conditions of themines and selectthemost appropriate technique to ensure that a robust and comprehen-sive risk assessment is conducted.

Riskmanagement often involves decision-making in situations char-acterized by high risk and large uncertainties, and such decision-makingpresents a challenge in that it is difficult to predict the consequences ofthe decisions. Nonetheless, the highest risk levels i.e.most severe conse-quences and highest likelihood of occurring, are suggested to becontrolled or minimized as much as possible.

This researchwork presents a simple practical risk assessment usingfuzzy TOPSIS (Chen, 2000), which stands for “Technique of OrderPreference Similarity to the Ideal Solution”. In this respect, the TOPSISapproach is extended to develop a risk-based methodology underfuzzy environment.

TOPSIS as one of themost applicableMultiple Criteria DecisionMak-ing (MCDM)methods assigns the best alternative amongmany feasiblealternatives by calculating the distances from the positive ideal and thenegative ideal (anti-ideal) solutions. TOPSIS is usually criticized due toneglecting uncertainties. To overcome this deficiency, fuzzy logic,which is able to model the uncertainties, is employed in our research.

The fuzzy technique uses linguistic variables instead of quantitativeexpression. This technique is a very helpful concept for dealingwith sit-uations which are too complex or not well-defined enough (Zadeh,1965). Therefore, fuzzy TOPSIS is applied in order to analyze the risk as-sociated with health and safety of coal miners, because of its capabilityand efficiency in handling uncertainties, simultaneous consideration ofthe positive and the negative ideal solutions, simple computations andlogical concept. The proposed methodology aims to identify the impor-tant risks and introduce effective measures for managing them.

2. Literature review

Risk assessment and safety evaluation are important in almost allindustries. Mining as a high risk operation is no exception and in thisrespect several studies with different approaches had been done.Notwithstanding, accidents and hazards in underground mines arevery complex events and many factors can contribute to occurrence ofundesirable events (Sari et al., 2004). Due to importance of the problem,different researches were conducted to find out the relationshipbetween the human health and environmental conditions via effectiveparameters in the occurrences of injuries or fatalities in undergroundcoal mines.

Recently, with the application and development of safety engineer-ing systems in coal mining, much attention has been paid to researchin coal mine safety assessment, which resulted in a number of valuablefindings.

Paul (2009) used the retrospective case–control study design to pre-dict work injuries among mine workers. The prediction of work injuryin mines was done by a step-by-step multivariate logistic regressionmodeling. A methodology is also proposed toward development of anuncertainty model that includes randomness in the occurrence ofdays-lost accidents in a coal mine (Sari et al., 2009). Grayson et al.(2009) usingMine Safety and Health Administration (MSHA) database,analyzed risks posed by major hazards of fires and explosions in minesthrough scrutiny ofmajor hazard-related violations ofmandatory safetystandards. Coleman and Kerkering (2007) conducted statistical analysisto measure safety in underground coal and non-coal mines. Kniesnerand Leeth (2004) described information needed to examine thecost-effectiveness of mine safety policy. They used assembled data onunderground coal mine production, injuries and safety inspections

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Fig. 1. The main elements of the proposed risk management process.

Fig. 2. Schematic view of a triangular fuzzy number.

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and other regulatory activities to estimate an econometrically sophisti-cated regression model of the connection between mine inspectionsand mine safety outcomes. Duzgun and Einstein (2004) proposed arisk and decision analysis methodology for management of risk associ-ated with roof falls in underground coal mines using exponential andPoisson distributions and cost benefit analysis. Shahriar and Bakhtavar(2009) employed a quantitative approach and decision tree in orderto assess and manage roof fall risks as a major geotechnical problemin Iranian underground coal mines. Khanzode et al. (2011) presentedamethodology for evaluating andmonitoring of recurrence characteris-tics of hazards in underground coal mines. Their methodology includesa systematic procedure beginning from identification of hazards basedon Weibull and Poisson distribution models, to their quantificationand then periodic monitoring of hazards using control chartingprinciples.

Zhu and Xiao-ping (2009) investigated safety evaluation of humanaccidents in coal mines using data mining approaches. They employedant colony optimization and support vector machine algorithms for fea-ture extraction of influencing factors and evaluation model of humanaccidents.

Recently the fuzzy TOPSIS technique has demonstrated its capabili-ties and efficiencies as a practical engineering and problem-solving

tool. Some of the applications of this technique in the field of risk man-agement are presented as follows. Wang and Elhag (2006) proposed afuzzy TOPSIS method based on alpha level sets and presented a nonlin-ear programming solution procedure for bridge risk assessment.Yazdani et al. (2012) proposed a fuzzy TOPSIS framework to extendconventional risk analysis andmanagement for critical asset protection.Zhang et al. (2013) developed a new evaluation model based on the

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interval analytic hierarchy process and extension of TOPSIS with inter-val data to improve the reliability of risk identification on a hydropowerproject. SoltanPanah et al. (2011) used fuzzy TOPSIS to assess risk forplanning the repair and maintenance of bridges. Fouladgar et al.(2011) applied fuzzy TOPSIS approach in order to evaluate the existingrisk in a water conveyance tunnel project via extraction of elevenmajordimensions of risks associated with tunneling. Kutlu and Ekmekcioglu(2012) applied fuzzy TOPSIS integrated with fuzzy analytical hierarchyprocess to present fuzzy Failure Mode and Effects Analysis (FMEA) forevaluation of the risk factors of each potential failure mode in linguisticvariables. Zhou and Lu (2012) employed fuzzy TOPSIS and fuzzy analyt-ic network process for risk evaluation of dynamic alliances, which canhelp enterprises to choose a coalition partner and make a reasonablebenefit allocation plan. Finally, Lee et al. (2013) developed a newproce-dure that combines Delphi method with fuzzy TOPSIS technique forflood risk and vulnerability management.

3. Methodology

There are several ways of presenting the risk management process,but most structures contain the three key elements of planning, riskassessment (execution) and risk treatment (Aven, 2008).

The risk management procedure proposed in our research iscomposed of six steps: establishing the context, risk identification, riskanalysis, risk evaluation, ALARP level and risk treatment associatedwith the health and safety of workers at underground coal mines.The main elements of the risk management process are shown inFig. 1 and the proposed framework is explained in detail in the nextsubsections.

3.1. Establishing the context

The first step of the risk management process is to define the objec-tives of the analysis. Establishing the context defines the limits withinwhich risks must be managed and sets the scope for the rest of therisk management process. The context includes the environmentandworking conditions of themine and thepurpose of the riskmanage-ment activity. When formulating the objectives, any limitations to thescope of the analysis should be taken into consideration, such as lackof available resources, time limits and lack of data.

3.2. Risk assessment

Risk assessment is a systematic use of available information to deter-mine how often specific events may occur and the magnitude of theirlikely consequences. As shown in Fig. 1, the risk assessment is the cen-tral part of the risk management process, which purposes to establisha proactive safety strategy by investigating potential risks. In ourresearch, risk assessment is a process of assessing the likelihood of themining hazard causing or contributing to any harm to any miner; andthe consequence of the harm which may be caused.

The human health and safety risks can be considered or assessed atvarious levels depending on the scope and purpose of the assessment.The risk assessment will consider the relationship between the likeli-hood and potential consequence of the risk of hazards occurring, andto review the current or planned approaches to controlling the hazards.

In essence, risk assessment includes three steps: risk identification,risk analysis and risk evaluation. That means, to assess a risk, potentialsources of harm should be identifiedfirst and then the likelihood and con-sequence of them occurring should be estimated to analyze the risk.Thereafter risk should be evaluatedwhichmeans comparing the estimat-ed risks against risk criteria to determine the significance of risk.

3.2.1. Risk identificationOnce the context of the risk assessment is properly documented, the

next step in the risk management process is to identify the hazards

associated with anymining activities under consideration. Risk identifi-cation includes identifying the hazards and the situations that have thepotential to cause harm or losses, sometimes called ‘unwanted events’(Joy, 2004).

In this step based on historical information ofminemonitoring, a de-cisionmatrix is established to determine potential risk factors. This stepis the base of planning and executing phases, thus the informationshould be detailed appropriately.

Presence of hazards in awork system is themain cause of occurrenceof accidents. Identifying hazards and compiling information about themis thefirst step in planning for safety.When a hazard is transformed intoa harmful event, an accident takes place (Khanzode et al., 2011). Theoutcome of this hazard identification process should be a comprehen-sive and creditable list of human health and safety risks associatedwith the mining activities being assessed, which forms the basis offuture risk management activities.

In general, qualitative risk assessment is typically used for most cir-cumstances in the mining industry and quantitative risk assessmentmay not even be possible because of the absence of reliable data. Thequalitative expressions are the same linguistic variables defined in thefuzzy logic. Thus the calculation of the risk can be simplified byconverting the linguistic scales into Triangular Fuzzy Numbers (TFNs).

Fuzzy logic which was introduced by Zadeh (1965), can take intoaccount uncertainty and solve problems where there are no sharpboundaries and precise values. A linguistic variable is defined as a vari-able whose values are not numbers, but words or sentences in a naturallanguage such as veryweak, weak,moderate, strong, etc. The concept ofa linguistic variable provides a means of approximate characterizationof phenomenawhich are too complex or too ill-defined to be amenableto describe in conventional quantitative terms (Zadeh, 1975).

A fuzzy set is a class of objects with a continuum of grades of mem-bership. Such a set is characterized by a membership function, whichassigns to each object a grade of membership ranging between zeroand one. In other words, a fuzzy number belongs to the closed interval0 and 1, in which 1 addresses full membership and 0 expresses non-membership. By contrast, crisp sets only allow 0 or 1. Thus fuzzy setsare a general form of crisp sets.

There are various types of fuzzy numbers that can be utilized basedon the situation. In practice, TFN is the most interesting due to intuitiveand computational simplicity. As shown in Fig. 2, a TFN can be defined

as a triplet eM ¼ l;m; uð Þ or eM ¼ lm ;mu� �

, where l, m, and u respectivelydenote the smallest possible value, the most promising value, and thelargest possible value that describe a fuzzy event.

Each TFN, eM, has linear representations on its left and right sidessuch that its membership function, μeM xð Þ, can be defined as:

μeM xð Þ ¼x−lð Þ= m−lð Þ if l ≤ x ≤ mu−xð Þ= u−mð Þ if m ≤ x≤u0 otherwise

8<: ð1Þ

where, l,m, and u denote the smallest possible value, the most promis-ing value, and the largest possible value, respectively as shown in Fig. 2.A fuzzy number eM is a convex normalized fuzzy set of the real lineR → [0, 1] such that (Buckley, 1985; Zimmermann, 1992):

o μeM xð Þ is piecewise continuous.o μeM xð Þ is normalized, that is, there exists x∈R with μeM xð Þ ¼ 1 (x is

called the mean value of eM).

3.2.2. Risk analysisRisk analysis includes analyzing themagnitude of risk thatmay arise

from the unwanted event. The objective of a risk analysis is to describerisk, that is to present an informative risk picture (Aven, 2008), whichcan be illustrated in the form of a bow-tie diagram.

The bow-tie diagram is a synergistic adaptation of fault tree analysisand event tree analysis that shows how a range of causes, controls and

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outcomes can be linked together and associated with each majorincident scenario.

Cumulative consideration of the hazards can be seen as the overallevaluation of interactions between different parts of a single bow-tie di-agram or consideration of a range of bow-tie diagrams together. Hencecumulative consideration of hazards can be used to assess the overallpicture of the facility risks, and to understand how different causesand events can combine to lead to an incident. It also enables the keycauses and controls for the risk to be identified and evaluated in moredetail if required. It is not of place to mention that risk analysis doesnot give direct answers as to what is the correct solution, but it onlygives a risk description whichwill provide a basis for the choice of solu-tions (Aven, 2008).

3.2.3. Risk evaluationThe purpose of risk evaluation is to make decisions, based on the

outcomes of risk analysis, about which risks need treatment and whatthe treatment priorities are. The various methods available for riskevaluation usually use the same broad principles, so that consequenceand likelihood are identified and combined to produce a level of risk.In other words, risk equals to multiply of likelihood (probability) of anoccurrence and consequences of the occurrence.

Generally, in our proposed methodology this step involves rankingall risks using fuzzy TOPSIS into a consolidated listingwith all identifiedand assessed risks then ranking them from highest to lowest.

3.2.3.1. TOPSIS. TOPSIS as an applicable MCDM approach was firstproposed by Hwang and Yoon (1981) and thereafter expanded byChen et al. (1992). It is a practical and useful technique for rankingand selection of a number of externally determined alternatives throughdistance measures (Zhou and Lu, 2012).

The basic concept of this method is that the chosen alternativeshould have the shortest distance from the positive ideal solution (thebest possible status) and the farthest distance from the negative idealsolution (the worst possible status) (Lai et al., 1994). The positiveideal solution is a solution that simultaneously maximizes benefitcriteria and minimizes cost criteria, whereas the negative ideal solutionmaximizes the cost criteria and minimizes the benefit criteria. TheTOPSISmethod assumes that each criterion has a tendency tomonoton-ically increase or decrease utility. Therefore, it is easy to define thepositive and negative ideal solutions.

Fig. 3. Hierarchy procedure of risk control (

The TOPSIS method is based on six computation steps. The first stepis the gathering of the performances of the alternatives on the differentcriteria. These performances need to be normalized in the second step.The normalized scores are then weighted and after determination ofthe positive and negative ideal solutions, the distances to the ideal andanti-ideal points are calculated. Finally, the closeness is given by theratio of these relative distances (Hwang and Yoon, 1981; Lai et al.,1994).

The classical TOPSIS method operates in a deterministic context andevaluation process which involves judgments precisely defined andcrisp values. However, under some conditions crisp values are inade-quate to model real world decision problems, because actual problemsusually involve uncertain, imprecise and subjective data, which makethe decision-making process more complex and challenging. On theother hand, human judgment and preferences are often ambiguousand cannot be estimated with exact numerical values.

Therefore, the fuzzy TOPSIS method is proposed where the conse-quence and likelihood are evaluated by linguistic variables representedby fuzzy numbers to address such uncertainty and vagueness in thetraditional TOPSIS.

3.2.3.2. Fuzzy TOPSIS. Fuzzy logic is a powerful mathematical tool forhandling the existing uncertainty in decision making. Overcoming theuncertainty of qualitative data, the ranking process may be accom-plished by the fuzzy TOPSIS method. The mathematical concept offuzzy TOPSIS proposed by Chen (2000) can be summarized as follows.

After identification of the consequence and likelihood, the fuzzynumbers should be calculated corresponding to each linguistic variable.Before analyzing and modeling, the data have to be normalized to keepthem in the prescribed range of 0 and +1. The normalization of fuzzynumbers is accomplished by using linear scale transformation toconvert the different units into a comparable unit.

erij ¼ lijuþj;mij

uþj;uij

uþj

!; uþ

j ¼ maxi

uij; ∀ jþ ð2Þ

erij ¼ l−juij

;l−jmij

;l−jlij

!; l−j ¼ min

ilij; ∀ j−: ð3Þ

Applied Manual, 2007; Barnes, 2009).

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Fig. 4. Geological map of Kerman coal deposit (Engineering Report, 1970).

90 S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

As mentioned l, m, and u are the smallest possible value, the mostpromising value, and the largest possible value, respectively.

For benefit criteria the larger r ̃ij has the greater preference while forthe cost criteria the smaller r ̃ ij has the greater preference. Hence, thenormalized fuzzy decision matrix can be obtained as:

eR ¼ erijh in�m

ð4Þ

where, r ̃ ij is the normalized value of exij ¼ lij;mij;uij

� �.

The weighted normalized value evij is calculated by multiplying theweights ewj

� �of criteria with the normalized fuzzy decision matrix erij .

Theweightednormalizeddecisionmatrix eV for each criterion is calculatedthrough the following relation:

eV ¼ ewjerijh i¼ evijh i

n� ji ¼ 1;2;…;m j ¼ 1;2;…;n: ð5Þ

In this matrix, each element evij is a fuzzy normalized number whichranges within the closed interval [0, 1]. Thereafter the fuzzy positiveideal solution (A+) and fuzzy negative ideal solution (A−) are obtainedas:

Aþ ¼ evþ1 ;evþ2 ;evþ3 ;…;evþn� �¼ max

ivijj i ¼ 1;2;…;m; j ¼ 1;2;…;nð Þ

� �ð6Þ

A− ¼ ev−1 ;ev−2 ;ev−3 ;…;ev−nð Þ ¼ mini

vijj i ¼ 1;2;…;m; j ¼ 1;2;…;nð Þ� �

:

ð7Þ

The distance of each alternative from A+ and A− are calculated as:

dþi ¼Xnj¼1

d evij;evþj� �ð8Þ

d−i ¼Xnj¼1

d evij;ev−j� �ð9Þ

where, di+ and di− are the primary and secondary distant measures, re-

spectively. The distance measurement between two TFNs of (l1,m1, u1)and (l2, m2, u2), can be calculated by the vertex method as follows:

dv em; enð Þ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi13

l1−l2ð Þ2 þ m1−m2ð Þ2 þ u1−u2ð Þ2h ir

: ð10Þ

Finally the alternatives can be ranked using closeness coefficient (Ci)index in decreasing order. The larger the index value, the better the per-formance of the alternatives. The Ci takes into account the di+ and di

− si-multaneously. The relative Ci index of each alternative with respect tothe fuzzy positive ideal solution is obtained as:

Ci ¼d−i

dþi þ d−i� � Ci ¼ 1 if Ai ¼ Aþ

Ci ¼ 0 if Ai ¼ A−:

ð11Þ

As di− ≥ 0 and di+ ≥ 0, then clearly Ciϵ[0, 1].

3.3. ALARP level

Once the risks have been identified and evaluated, proper risk con-trol and treatment strategies should be made to deal with the potentialrisks in themines. The aim of risk control and treatment is to remove as

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Table 1Classification of various types of the risks affecting human health and safety in coalmines.

Risk type Code Event

Geo-mechanical GM1 Outburst/rock burstGM2 Struck by materials (rock, wood, etc.)

falling off from roof or ribGM3 WindblastGM4 Catastrophic failureGM5 Collapse or slump of wallGM6 Flyrock occurrencesGM7 Spalling of ribs or sidesGM8 Instability of pillarsGM9 Instability of coalfaceGM10 Instability of galleriesGM11 Trapping/entanglement in caved areaGM12 Floor failure/heaveGM13 Instability of immediate roofGM14 SubsidenceGM15 Incomplete stowing

Geo-chemical GC16 Coal and sulfide ore dust explosionGC17 Firedamp explosionGC18 Emission of gases such as H2S, CO, CO2, NO, etc.

Electrical E19 ElectrocutionE20 Dealing with misfireE21 Power disruptionsE22 Dead bulbs/fluorescent tubesE23 Energy from switches, power boards,

generators, etc.E24 Blasting with non-standard wire/firing lineE25 Electricity problems of water pumps

Mechanical M26 Tearing of pressure vesselsM27 Acute jolts and whole body vibration

via machinesM28 Unintended operation of equipmentM29 Water pressure from pump stations

and reticulationM30 Hazards during maintenance and repairsM31 Slipping belt conveyorM32 Stopping of ventilation systemM33 Tearing of towing wireM34 Wagons separation in inclinesM35 Technical defect of machinesM36 Jackshaft of the locomotiveM37 Old vehicle seats and poor seating

Chemical C38 Inappropriate firingC39 Unbalanced oxygen of blastingC40 Non-standard explosivesC41 Hazardous fuels and chemicals

Environmental EN42 Slippery floorEN43 Poorly lit areas and illumination problemsEN44 Caught between moving partsEN45 Inrush of water, mud, gas, etc.EN46 DrowningEN47 Tire explosionEN48 Asphyxiation due to inspiration of coal dust

and toxic gasesEN49 Radiation, reflection and excessive glareEN50 Thermal heat sourcesEN51 Bacteria in waterEN52 Noise pollutionEN53 Release flammable gases such as acetylene

and methaneEN54 Hearing lossEN55 Poisoning due to fire and carbon monoxideEN56 Misty and fumy conditions

Personal P57 Smoking during refuelingP58 Inattention to safety signsP59 Not using safety garmentP60 Using compressed air to clean clothesP61 Handling batteries without cautionP62 Slip/trip while entering or leaving equipmentP63 Slip, trip or fall during operation

and/or maintenanceP64 Falling from heightsP65 Vehicle–pedestrian collisionsP66 Lightning strike on stored explosivesP67 Fatigue or illnessP68 Injuries due to contiguity with equipmentP69 Putting detonator in pocket

(continued on next page)

Table 1 (continued)

Risk type Code Event

P70 Backfall in gradient or even routesP71 Incaution during transportation,

storage and handling of explosivesP72 Depleting misfire blast-holesP73 Unfamiliarity with the emergency exit locations

Social, culturaland managerial

S74 Lack of safety garmentsS75 Lack of firefighting equipmentS76 Unauthorized entry to the extraction areaS77 Transportation of personnel by conveyor/wagonS78 Lack or deficiency of communication deviceS79 Blasting without controlling methane/dust densityS80 Traffic in excavation areaS81 Lack of knowledge and inaccessibility of first aidS82 Inadequate trainingS83 Manual handlingS84 Poor ergonomicsS85 Using unsuitable wood for supportS86 Poor site management system

91S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

many negative impacts as possible and to assure that the risks are AsLow As Reasonably Practicable (ALARP). This principle means thatthe benefits of a measure should be assessed in relation to the disad-vantages of the measure. The ALARP principle is based on “reversedburden of proof,” which means that an identified measure shouldbe implemented unless it cannot be documented that there is anunreasonable disparity between disadvantages and benefits (Aven,2008).

The mining activities at a mine should be regularly monitored andevaluated to confirm that the recommended risk control practices areadequate to ensure risks are kept at acceptable levels. Records of themonitoring programs are to be kept and must include details ofspecific controls, equipments, procedures, engineering barriers orother measures to be in place for management of the activities withan unacceptable level of risk.

3.4. Risk treatment

As shown in Fig. 1, risk assessment is followed by risk treatment,which represents the process and implementation of measures tomodify risk, including tools to avoid, reduce, optimize, transfer and orretain risk.

According to Fig. 3, the hierarchical procedure of risk control beginswith elimination of the risk, which is always the best solution, and endswith reliance on personal protective equipment, which should be thelast resortmeasure (AppliedManual, 2007). This hierarchical procedurecan assist in determining the appropriate measures to manage the risksassociated with coal miners.

The first step in the risk hierarchy is, where possible, to avoid risk.This requires a structured process of hazard and risk identification,and the first objective is to implement design options to eliminate haz-ards. The secondobjective is to propose riskmeasures to reduce the risk.In the third step, substitution of a new activity, procedure, plan, processor substance is considered to control the risk. In the fourth step, person-nel are isolated from the mining hazards. The fifth and sixth objectivesare engineering and administrative controls, respectively, which musttry to control the risks at their sources. The last objective is to developdesigns which aim to protect all those exposed to the residual risks in-volved in the mining operations, as opposed to relying on risk controlmeasures that only give some protection to individuals. Appropriateequipment that protects all miners is the least preferred method ofcontrol.

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Fig. 5. Bow-tie diagram for injury or fatality of underground coal miners.

92 S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

4. Application of the proposed methodology

4.1. Kerman coal deposit

The coal region of Kerman is a significant deposit, which is located atthe southeast of central Iran, extending approximately 70 km fromthe southeast to northwest with a width of 10–15 km (EngineeringReport, 1970). There are several coal seams in the region and some ofthem are currently extracted in underground mines such as Hojedk,Babnizu and Hashouni as shown in Fig. 4.

Geologically the deposit is confined to the large Kerman syncline,formed mainly of Jurassic coal bearing deposits. Within the bounds ofthe region the syncline is composed mainly of Jurassic sandstone-siltstone-argillite series, including groups of coal seams and limestone(Engineering report, 1970).

The lower (Lias) and middle (Dogger) formations of Jurassic de-posits in the Kerman region are coal-bearing, comprising up to six coalseries, each of which contains a different number of coal seams andsheds. Themost abundant coal-bearing series are located in the depositsof the middle Jurassic formation (Dogger), which stretch throughoutthe entire deposit and contains seams of coking coals (Engineeringreport, 1970).

Within the bounds of the deposit, there are nine distinctive areas,representing fragments of the south-western (Dachrud, Neyzar,Daregor and Darbidkhun) and north-eastern (Khamrud and Eshkeli)limbs of the Kerman syncline and its two troughs, i.e. the north-western trough (Pabedana) and south-eastern trough (Hojedk andBabnizu).

The areas are separated by large tectonic dislocations. The deposit islocated in a mountainous region, deeply cut by canyons with heights of

Table 2Qualitative description of the likelihoods (Applied Manual, 2007; Barnes, 2009).

Likelihood Linguistic expression Description

L1 Almost certain Expected to occur in most circumstancesL2 Likely (probable) Will probably occur in most circumstancesL3 Possible Could occur at some timeL4 Unlikely Is not likely to occur in normal circumstancesL5 Rare May occur only in exceptional circumstances

elevation ranging from 1950 to 2800–3000 m (Engineering Report,1970). The mountains are chiefly composed of Paleozoic dolostone,limestone and Cretaceous limestone or Jurassic sandstone.

According to the Statistical Energy Survey (2013), Iran had coalconsumption of 0.9 million tonnes (Mt) oil equivalent. Although coalis one of the most abundant fossil fuels in Iran, coal mineral resourcesare the least developed in the country. Proven reserves are estimatedto be approximately 1075 Mt, mainly coking and bituminous coals.

Annual coal production of Iran is approximately 1.5 Mt, fromrelatively small underground mines. Most production in the country isfrom underground, from relatively thin, locally steeply-dipping seams,and is destined for the steel industry.

The probable and proven reserves in the Kerman coal deposit areestimated to be 202 and 107 Mt respectively. In this research, threeunderground coal mines of this deposit namely Hashouni, Hojedk andBabnizu were investigated and the information collected from thesethree mines was used to manage risks affecting the health and safetyof their miners.

4.2. Classifying input data

The kind of data classification is usually optional and risk can be cat-egorized according to any part of an operationwhichmay be concerned.For example Paul (2009) based on the analysis of mining and non-mining industries identified the variables affecting human health andsafety in mines as demographics, personality, employment, safety-environment, social support, work-hazards, safe work behavior andwork-injury. Maiti and Bhattacharjee (2001) also identified mineaccidents/injuries associated with many parameters as personal, socialand technical factors.

Table 3Qualitative description of the consequences (Applied Manual, 2007; Barnes, 2009).

Consequence Linguistic expression Description

C1 Severe (catastrophic) Death or permanent disability to one ormore persons

C2 Major (considerable) Hospital admission requiredC3 Moderate Medical treatment required/hospitalizationC4 Minor (tolerable) First aid requiredC5 Insignificant Injuries not requiring first aid

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Table 4Fuzzy scale for rating linguistic terms (Kaufmann and Gupta, 1985).

Linguistic scale Triangular fuzzy scale (for both Cs and Ls)

Consequence Likelihood

C1 L1 (0.75, 0.9, 1.00)C2 L2 (0.55, 0.7, 0.85)C3 L3 (0.35, 0.5, 0.65)C4 L4 (0.15, 0.3, 0.45)C5 L5 (0.00, 0.1, 0.25)

93S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

In relation to human health and safety in underground miningoperations at the Kerman coal deposit, the risk types are categorizedin eight groups: geomechanical (GM), geochemical (GC), electrical (E),mechanical (M), chemical (C), environmental (EN), personal (P), andsocial, cultural and managerial (S) risks. Altogether 86 hazards wereidentified based on raw data reported by the Ministry of Cooperatives,Labour and Social Welfare of Iran (2009). The details of the eventsaffecting human health and safety of the three mentioned coal minesare summarized and codified in Table 1.

4.3. Risk picture

In our cases where a large number of different hazards and potentialincidents exist, the cumulated risk may be significant even if the riskarising from each is low. These issues can be illustrated in the form ofa bow-tie diagram.

A bow-tie diagramwhich is highly effective for initial process of haz-ard analysis can be envisaged for every major mining hazard scenarioidentified for the mine (Applied Manual, 2007). A mine will typicallyhave a range of mining hazards that need to be identified prior toassessment. The overall profile of risks from all of these mining hazardscan be determined using a bow-tie diagram. The simplified bow-tiediagram resulted from our research is shown in Fig. 5.

Located at the center of the bow-tie diagram is the initiating event,which in our research is the injury or fatality of underground coalminers, i.e. disease and injuries affecting or minatory to personnel. Theleft side of the bow-tie diagram illustrates the causal risk pictures thatmay lead to occurrence of the initiating event and the right sidedescribes the possible consequences of the initiating event.

On the left side are barriers (left ellipsoid) that are introduced toprevent the initiating event from occurring; these are the probability re-ducing or preventive barriers. On the right side are barriers (right ellip-soid) to prevent the initiating event from bringing about seriousconsequences; the consequence reducing or mitigating barriers.

Fig. 6. Linguistic variables for likelihoods and consequences.

4.4. Fuzzy TOPSIS modeling

In this research incidents affecting human health are characterizedby two dimensions, the likelihood of occurrence and the consequencesof the incidents. In this regard, the consequences and probabilities arecategorized on a scale from 1 to 5 qualitatively. Together these twodimensions constitute the risk.

As shown in Table 2, likelihood is defined as a qualitative descriptionof probability and frequency from L1 to L5. Consequence which is theoutcome of the initiating event is also expressed qualitatively from C1to C5 in Table 3.

After description of the likelihoods and consequences, the most im-portant task in risk assessment is to calculate the risk rank of all relatedevents and arrange those in order of priority to gain an understanding ofthe most important hazards.

Risk ranking is not an exact quantification of risk but it is a means ofprioritizing actions and allocating resources to control hazards. In realsituations, the ratings are usually difficult to be judged very preciselybecause of the existence of uncertainty and vagueness, but can be suit-ably characterized by linguistic terms which are fuzzy in nature andthen transferred into fuzzy numbers. Such a method was extensivelyextended by many practitioners to deal with fuzzy MCDM problems(Wang et al., 2003; Wang and Lee, 2007).

Therefore considering the limitations of quantitative approaches,fuzzy TOPSIS procedure is used for evaluating and ranking of risksaffecting miners' health and safety. For this purpose, fuzzy TOPSIS isapplied to calculate fuzzy positive and fuzzy negative ideal situationsfor finding Ci index (Eq. (11)). Thenceforth on the basis of Ci index,risks are evaluated and ranked. The abbreviation of the linguisticterms of consequence and likelihood, and their equivalent TFNs areshown in Table 4. Fig. 6 shows the conceptual schemaof TFNs presentedin Table 4.

The details of the fuzzification of the likelihoods and consequences forall 86 potential risks are presented in Table 5. Asmentioned, the informa-tion in the second column (Likelihood) and the fourth column (Conse-quences) are based on raw data reported by the Ministry ofCooperatives, Labour and Social Welfare of Iran (2009). The raw datawere linguistic terms based on Tables 2 and 3, which transferred intofuzzy numbers according to Table 4. TFNs in the last column are risk asa function of the fuzzy likelihood and fuzzy consequence for each row.

According to Eq. (4), the normalized fuzzy decision matrix is denoted

by eR ¼ erij n�m . Therefore, as the fuzzy linguistic ratings, presented in

Table 4, preserve the property that the ranges of normalized TFNs belong-ing to the closed interval [0, 1], the normalization procedure is notnecessary.

Based on the TFNs presented in Table 5, A+ and A− are determinedas (0.4125, 0.63, 0.85) and (0.00, 0.03, 0.1125) using Eqs. (7) and (8),which indicate the most and the least preferable alternatives,respectively.

For evaluating and ranking risks on the basis of the Ci index, theresults of calculation of the Ci index are shown in Table 6. According toEq. (11), the Ci is calculated simultaneously based on the distance d+

and d− to both A+ and A− using Eqs. (9) and (10). Finally a preferenceorder can be ranked according to the order of the Ci index.

According to the basic principle of the fuzzy TOPSISmethod, the highrisk event is the one which has the shortest distance from the fuzzypositive ideal solution and farthest distance from the fuzzy negativeideal solution. Therfore based on the results, the ranking of the eventsare determined so that risks having Ci value closest to 1 is ranked highestrisk, while risks having Ci value farthest from 1 is ranked lowest risk.

The results shown in Table 6, are arranged in descending order inTable 7. As can be seen from this table, twelve groups with differentrisks are obtained,which are shown fromA to L. GroupAhas the highestrisk and group L has the lowest risk affecting the health and safety of theminers.

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Table 5Risk calculation by fuzzification of the likelihoods and consequences.

Code Likelihood TFN of likelihood Consequence TFN of consequence TFN of risk = L × C

GM1 L4 (0.15, 0.3, 0.45) C1 (0.75, 0.9, 1.00) (0.1125, 0.27, 0.4500)GM2 L1 (0.75, 0.9, 1.00) C2 (0.55, 0.7, 0.85) (0.4125, 0.63, 0.8500)GM3 L5 (0.00, 0.1, 0.25) C1 (0.75, 0.9, 1.00) (0.0000, 0.09, 0.2500)GM4 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)GM5 L3 (0.35, 0.5, 0.65) C4 (0.15, 0.3, 0.45) (0.0525, 0.15, 0.2925)GM6 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)GM7 L1 (0.75, 0.9, 1.00) C5 (0.00, 0.1, 0.25) (0.0000, 0.09, 0.2500)GM8 L5 (0.00, 0.1, 0.25) C3 (0.35, 0.5, 0.65) (0.0000, 0.05, 0.1625)GM9 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)GM10 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)GM11 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)GM12 L3 (0.35, 0.5, 0.65) C4 (0.15, 0.3, 0.45) (0.0525, 0.15, 0.2925)GM13 L1 (0.75, 0.9, 1.00) C2 (0.55, 0.7, 0.85) (0.4125, 0.63, 0.8500)GM14 L1 (0.75, 0.9, 1.00) C4 (0.15, 0.3, 0.45) (0.1125, 0.27, 0.4500)GM15 L1 (0.75, 0.9, 1.00) C3 (0.35, 0.5, 0.65) (0.2625, 0.45, 0.6500)GC16 L3 (0.35, 0.5, 0.65) C1 (0.75, 0.9, 1.00) (0.2625, 0.45, 0.6500)GC17 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)GC18 L1 (0.75, 0.9, 1.00) C2 (0.55, 0.7, 0.85) (0.4125, 0.63, 0.8500)E19 L4 (0.15, 0.3, 0.45) C1 (0.75, 0.9, 1.00) (0.1125, 0.27, 0.4500)E20 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)E21 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)E22 L2 (0.55, 0.7, 0.85) C4 (0.15, 0.3, 0.45) (0.0825, 0.21, 0.3825)E23 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)E24 L4 (0.15, 0.3, 0.45) C2 (0.55, 0.7, 0.85) (0.0825, 0.21, 0.3825)E25 L5 (0.00, 0.1, 0.25) C4 (0.15, 0.3, 0.45) (0.0000, 0.03, 0.1125)M26 L4 (0.15, 0.3, 0.45) C2 (0.55, 0.7, 0.85) (0.0825, 0.21, 0.3825)M27 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)M28 L5 (0.00, 0.1, 0.25) C3 (0.35, 0.5, 0.65) (0.0000, 0.05, 0.1625)M29 L4 (0.15, 0.3, 0.45) C5 (0.00, 0.1, 0.25) (0.0000, 0.03, 0.1125)M30 L5 (0.00, 0.1, 0.25) C4 (0.15, 0.3, 0.45) (0.0000, 0.03, 0.1125)M31 L2 (0.55, 0.7, 0.85) C4 (0.15, 0.3, 0.45) (0.0825, 0.21, 0.3825)M32 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)M33 L4 (0.15, 0.3, 0.45) C1 (0.75, 0.9, 1.00) (0.1125, 0.27, 0.4500)M34 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)M35 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)M36 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)M37 L2 (0.55, 0.7, 0.85) C5 (0.00, 0.1, 0.25) (0.0000, 0.07, 0.2125)C38 L4 (0.15, 0.3, 0.45) C2 (0.55, 0.7, 0.85) (0.0825, 0.21, 0.3825)C39 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)C40 L5 (0.00, 0.1, 0.25) C1 (0.75, 0.9, 1.00) (0.0000, 0.09, 0.2500)C41 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)EN42 L4 (0.15, 0.3, 0.45) C3 (0.35, 0.5, 0.65) (0.0525, 0.15, 0.2925)EN43 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)EN44 L5 (0.00, 0.1, 0.25) C2 (0.55, 0.7, 0.85) (0.0000, 0.07, 0.2125)EN45 L4 (0.15, 0.3, 0.45) C2 (0.55, 0.7, 0.85) (0.0825, 0.21, 0.3825)EN46 L5 (0.00, 0.1, 0.25) C4 (0.15, 0.3, 0.45) (0.0000, 0.03, 0.1125)EN47 L5 (0.00, 0.1, 0.25) C3 (0.35, 0.5, 0.65) (0.0000, 0.05, 0.1625)EN48 L1 (0.75, 0.9, 1.00) C2 (0.55, 0.7, 0.85) (0.4125, 0.63, 0.8500)EN49 L5 (0.00, 0.1, 0.25) C4 (0.15, 0.3, 0.45) (0.0000, 0.03, 0.1125)EN50 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)EN51 L2 (0.55, 0.7, 0.85) C4 (0.15, 0.3, 0.45) (0.0825, 0.21, 0.3825)EN52 L1 (0.75, 0.9, 1.00) C3 (0.35, 0.5, 0.65) (0.2625, 0.45, 0.6500)EN53 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)EN54 L4 (0.15, 0.3, 0.45) C3 (0.35, 0.5, 0.65) (0.0525, 0.15, 0.2925)EN55 L3 (0.35, 0.5, 0.65) C3 (0.35, 0.5, 0.65) (0.1225, 0.25, 0.4225)EN56 L1 (0.75, 0.9, 1.00) C3 (0.35, 0.5, 0.65) (0.2625, 0.45, 0.6500)P57 L5 (0.00, 0.1, 0.25) C1 (0.75, 0.9, 1.00) (0.0000, 0.09, 0.2500)P58 L3 (0.35, 0.5, 0.65) C1 (0.75, 0.9, 1.00) (0.2625, 0.45, 0.6500)P59 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)P60 L2 (0.55, 0.7, 0.85) C4 (0.15, 0.3, 0.45) (0.0825, 0.21, 0.3825)P61 L5 (0.00, 0.1, 0.25) C3 (0.35, 0.5, 0.65) (0.0000, 0.05, 0.1625)P62 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)P63 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)P64 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)P65 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)P66 L5 (0.00, 0.1, 0.25) C1 (0.75, 0.9, 1.00) (0.0000, 0.09, 0.2500)P67 L3 (0.35, 0.5, 0.65) C1 (0.75, 0.9, 1.00) (0.2625, 0.45, 0.6500)P68 L2 (0.55, 0.7, 0.85) C3 (0.35, 0.5, 0.65) (0.1925, 0.35, 0.5525)P69 L4 (0.15, 0.3, 0.45) C1 (0.75, 0.9, 1.00) (0.1125, 0.27, 0.4500)P70 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)P71 L5 (0.00, 0.1, 0.25) C1 (0.75, 0.9, 1.00) (0.0000, 0.09, 0.2500)P72 L4 (0.15, 0.3, 0.45) C1 (0.75, 0.9, 1.00) (0.1125, 0.27, 0.4500)P73 L4 (0.15, 0.3, 0.45) C2 (0.55, 0.7, 0.85) (0.0825, 0.21, 0.3825)S74 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)S75 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)S76 L5 (0.00, 0.1, 0.25) C2 (0.55, 0.7, 0.85) (0.0000, 0.07, 0.2125)

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Table 5 (continued)

Code Likelihood TFN of likelihood Consequence TFN of consequence TFN of risk = L × C

S77 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)S78 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)S79 L3 (0.35, 0.5, 0.65) C1 (0.75, 0.9, 1.00) (0.2625, 0.45, 0.6500)S80 L3 (0.35, 0.5, 0.65) C2 (0.55, 0.7, 0.85) (0.1925, 0.35, 0.5525)S81 L5 (0.00, 0.1, 0.25) C3 (0.35, 0.5, 0.65) (0.0000, 0.05, 0.1625)S82 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)S83 L2 (0.55, 0.7, 0.85) C2 (0.55, 0.7, 0.85) (0.3025, 0.49, 0.7225)S84 L4 (0.15, 0.3, 0.45) C3 (0.35, 0.5, 0.65) (0.0525, 0.15, 0.2925)S85 L3 (0.35, 0.5, 0.65) C4 (0.15, 0.3, 0.45) (0.0525, 0.15, 0.2925)S86 L2 (0.55, 0.7, 0.85) C1 (0.75, 0.9, 1.00) (0.4125, 0.63, 0.8500)

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The Ci is always between 0 and 1, if an action is closer to the idealthan the anti-ideal, then Ci approaches 1, whereas if an action is closerto the anti-ideal than to the ideal, Ci approaches 0.

5. Results and discussion

Riskmanagement cannot eliminate risks altogether but can only iden-tify appropriate strategies to manage them (Mojtahedi et al., 2010). Thatis to say after identifying, analyzing and evaluating the risks affecting

Table 6Calculation of the Ci index for each hazard.

Code d+ d− Ci Code d+ d− Ci

GM1 0.356 0.248 0.411 EN44 0.545 0.062 0.102GM2 0.000 0.598 1.000 EN45 0.410 0.193 0.321GM3 0.523 0.087 0.142 EN46 0.598 0.000 0.000GM4 0.000 0.598 1.000 EN47 0.571 0.031 0.052GM5 0.473 0.129 0.214 EN48 0.000 0.598 1.000GM6 0.268 0.333 0.554 EN49 0.598 0.000 0.000GM7 0.523 0.087 0.142 EN50 0.268 0.333 0.554GM8 0.571 0.031 0.052 EN51 0.410 0.193 0.321GM9 0.000 0.598 1.000 EN52 0.178 0.422 0.703GM10 0.268 0.333 0.554 EN53 0.126 0.474 0.790GM11 0.126 0.474 0.790 EN54 0.473 0.129 0.214GM12 0.473 0.129 0.214 EN55 0.370 0.231 0.384GM13 0.000 0.598 1.000 EN56 0.178 0.422 0.703GM14 0.356 0.248 0.411 P57 0.523 0.087 0.142GM15 0.178 0.422 0.703 P58 0.178 0.422 0.703GC16 0.178 0.422 0.703 P59 0.268 0.333 0.554GC17 0.000 0.598 1.000 P60 0.410 0.193 0.321GC18 0.000 0.598 1.000 P61 0.571 0.031 0.052E19 0.356 0.248 0.411 P62 0.268 0.333 0.554E20 0.000 0.598 1.000 P63 0.268 0.333 0.554E21 0.268 0.333 0.554 P64 0.126 0.474 0.790E22 0.410 0.193 0.321 P65 0.268 0.333 0.554E23 0.268 0.333 0.554 P66 0.523 0.087 0.142E24 0.410 0.193 0.321 P67 0.178 0.422 0.703E25 0.598 0.000 0.000 P68 0.268 0.333 0.554M26 0.410 0.193 0.321 P69 0.356 0.248 0.411M27 0.268 0.333 0.554 P70 0.268 0.333 0.554M28 0.571 0.031 0.052 P71 0.523 0.087 0.142M29 0.598 0.000 0.000 P72 0.356 0.248 0.411M30 0.598 0.000 0.000 P73 0.410 0.193 0.321M31 0.410 0.193 0.321 S74 0.268 0.333 0.554M32 0.000 0.598 1.000 S75 0.268 0.333 0.554M33 0.356 0.248 0.411 S76 0.545 0.062 0.102M34 0.000 0.598 1.000 S77 0.126 0.474 0.790M35 0.268 0.333 0.554 S78 0.268 0.333 0.554M36 0.268 0.333 0.554 S79 0.178 0.422 0.703M37 0.545 0.062 0.102 S80 0.268 0.333 0.554C38 0.410 0.193 0.321 S81 0.571 0.031 0.052C39 0.126 0.474 0.790 S82 0.000 0.598 1.000C40 0.523 0.087 0.142 S83 0.126 0.474 0.790C41 0.268 0.333 0.554 S84 0.473 0.129 0.214EN42 0.473 0.129 0.214 S85 0.473 0.129 0.214EN43 0.126 0.474 0.790 S86 0.000 0.598 1.000

human health and safety, each must be controlled or eliminated if possi-ble, and if not they should be reduced to the ALARP level.

In addition, in the philosophy of risk management, the fatalistic ac-ceptance of accident occurrence has been replaced by the realizationthat impending loss, inmost cases, is predictable and therefore prevent-able (Allanson, 2002). In the context of the risk management process,this is valuable in mitigating and reducing risks to themaximum extentpossible.

According to the results of the risk assessment obtained from this re-search, the events GM2, GM4, GM9, GM13, GC17, GC18, E20, M32, M34,EN48, S82 and S86 are high-risk hazards (group A in Table 7) and needthemost attention, while E25, M29, M30, EN46 and EN49 pose the leastrisks (group L in Table 7). Thewhole twelve groups of the risks arrangedin Table 7, are depicted in Fig. 7.

Some risks are more manageable than others in the sense that thereis a greater potential to reduce them. Thus among the risks thosewhichhave a Ci indexmore than 0.5, i.e. groups A, B, C and D, are more impor-tant than others, hence appropriate risk measures should be suggestedfor them.

Table 7Arrangement of the Ci index in descending order.

Order Code Ci Order Code Ci Order Code Ci

A

GM2

1.000 D

E23

0.554

GEN51

0.321GM4 M27 P60GM9 M35 P73

GM13 M36

H

GM5

0.214GC17 C41 GM12GC18 EN50 EN42E20 P59 EN54M32 P62 S84M34 P63 S85

EN48 P65

I

GM3

0.142

S82 P68 GM7S86 P70 C40

B

GM11

0.790

S74 P57C39 S75 P66EN43 S78 P71

EN53 S80J

M370.102P64

E

GM1

0.411

EN44S77 GM14 S76

S83 E19

K

GM8

0.052

C

GM15

0.703

M33 M28GC16 P69 EN47EN52 P72 P61

EN56 F EN55 0.384 S81

P58

G

E22

0.321L

E25

0.000P67 E24 M29S79 M26 M30

DGM6

0.554M31 EN46

GM10 C38 EN49

E21 EN45

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The results of risk ranking in our cases are depicted in Fig. 8 in thecase of fuzzy numbers. This figure shows that all risks affecting humanhealth and safety are in an uncertain context because the correspondingfuzzy numbers are considerably overlapping.

The amount of uncertainty in the fuzzy ranking is obtained and rep-resented by the spread of the triangles. Thismeans thatwhen two trian-gles overlap, a weak rank can be established and the related uncertaintycan be associated to the ordinate of the intersection between the twotriangles. A wide overlapping area translates a high uncertainty relatedto the ranking,whichultimatelymeans the uncertainty in the input datais too high to obtain a clear preference.

Themerit of using the fuzzy TOPSIS approach is to determine the im-portance or preference of alternatives using fuzzy numbers to be moreadapted to the real world cases instead of crisp numbers. Fuzzy modelsusing TFNs proved to be very effective for solving decision-makingproblems where the available information is imprecise. Moreover, oneof the other benefits of our methodology is its avoidance of a complexstructure and/or a black box algorithm. In addition, the proposed ap-proach may allow taking into account not only the uncertainty relatedto qualitative judgments but also the uncertainty that may reside inthe measurement of quantitative parameters.

According to Fig. 3, the best way to control the risks, starts at the topof the hierarchy of controls, i.e. investigate if the risk can be eliminatedfirst. This is the most effective way to control a hazard. If this method isnot possible, it may use the other measures especially engineering oradministrative controls to reduce or minimize them. In the followingsubsections some recommendedmeasures for each one of themost im-portant risks, located at group A, are presented to control or reducethese risks.

5.1. Control measures for GM2

Being struck by materials is of considerable concern because of theserious consequences of rock falling injuries. Control measures put inplace to reduce this risk include improvement of the efficiency of thesupport system and isolation of miners from unsupported places. Inthis regard, studies of Maiti and Bhattacherjee (1999) showed thatcoalface workers are at risk more than at other locations. In these situa-tions, the probability of adverse events is dramatically reduced by thepractice of roof meshing. The provision of protective cabs on vehiclesalso reduces the probability of injury from falling materials further.

5.2. Control measures for GM4, GM9 and GM13

The risks of catastrophic failure, instability of coalface and instabilityof immediate roof are impressed by strata control. Roof failure is alwaysa serious problem affecting safe production in coal mines, so that in un-derground operations the main cause of death is roof falling (MacNeill,2008). Also roof failure is almost always the main cause of accidents inthe Kerman coal mines, which results in death, disability, injury, equip-ment damage and financial losses, so that during 2003–2008, about 60%of accidents and 30% of fatalities in the Kerman coal mines had beencaused by roof collapse.

The often soft, faulted and folded sedimentary strata make roofmovement a risk to safe and economic coal extraction. There shouldbe in place strata control plans to support the sides and roofs of minesand they should be updated regularly when a new mining area isentered.

Several factors have contributed to occurrences of roof falls in under-ground coal mines, such as geological conditions, openings and stopegeometry, mining method, mine layout, in situ and induced stressesstate, abutment load and mine environment (Iannacchione et al., 2001;Deb, 2003; Phillipson, 2003; Duzgun, 2005; Maiti and Khanzode, 2009).Among the factors affecting the roof fall hazards in coal mines, stresscondition and mine layout are somewhat controllable by appropriatemine design. However, it is relatively more difficult to control the effect

of geological conditions on roof falls, since the geological conditions arenature's uncertainty, and hence they comprise inherent variability inroof fall occurrences (Duzgun, 2005).

Themodel of stress development and strata failure should be devel-oped and be included in the principal hazard management plan forstrata instability. In order to develop an effective strata support systemit is necessary to understand the failuremechanism of the strata such asbuckling, bending and shear failure over the rib lines.

5.3. Control measures for GC17, GC18 and EN48

Firedamp explosion, emission of gases such as H2S, CO, CO2, NO, etc.and asphyxiation due to inspiration of coal dust and toxic gases are theother important risks in our cases. Firedamp explosion which triggersthe much more dangerous than coal dust explosion, can engulf the en-tire mine. Coal seam gas represents a potentially significant risk to thesafety and productivity of the underground mines, such that ineffectivecontrol of this gas increases the risk of creating conditions that may re-sult in either a coal and gas outburst or a methane and coal dust explo-sion, sometimes with fatal consequences for workers (Lama andBodziony, 1998). In addition, chronic lung diseases, such as pneumoco-niosis (black lung) are once common for miners, leading to reduced lifeexpectancy. In this regard, dust concentration levels in the mines are ofprimary importance and have to be controlled to prevent pulmonarydisease of miners.

5.4. Control measures for E20

Based on the coal mining health and safety regulation (AppliedManual, 2007) if a shot misfires, the shot-firer must take the action of(a) barricading each entrance to the place where the shot was fired;(b) immediately reporting the misfire to any person about to work atthe location of the misfire; (c) if possible, remedying the misfired shotand (d) preventing any work, other than work required to remedy themisfired shot from being carried out in the vicinity of the shot.

5.5. Control measures for M32

An active and effective ventilation system is very important in un-derground mining especially in coal mines, and according to the coalmining safety and health regulation (Applied Manual, 2007) an under-ground mine's safety and health management system must provideways of (a) preventing intake air from traveling across the face of apermanent seal at the mine; and (b) minimizing the risks of inrushand leakage into intake airways of atmospheric contaminants fromgoaf areas and abandoned or sealed workings.

5.6. Control measures for M34

Separation of wagons at inclines is another important riskwhich canresult in fatality, disability, equipment damage and downtime in ourcases. Preventing this event may be achieved by two ways: enhancingthe quality of the repair and maintenance, and utilizing the olderwagons at even galleries rather than at inclines.

5.7. Control measures for S82 and S86

Finally, the two important risks namely inadequate training andpoor site management system are also classified in group A. Utilizingeducated engineers and technical instructions are the best measuresin this regard. Furthermore regular periodic training of the personnelcan be very effective to reduce and manage the risk in our cases.

This can be achieved by: training to improve ability of the miners torecognize hazards, using warning signs and emphasizing that attentionbe paid to them, training to improve skills in avoiding injury, improvingmotivation to work in teams and so on. In addition, all persons who

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Fig. 7. Categorizing all risks in twelve groups according to Ci index.

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supervise, manage or use work equipment should be given adequatetraining for purposes of human health and safety, including training inthe methods which may be adopted when using the work equipment,any risks which such use may entail and precautions to be taken.

Paying attention to the risks in group A and implementing them inpractice can enhance the health and safety of the miners. These majorrisks have overlaps with some risks in the other groups. Nonethelessother riskmeasures whichmay be appropriate for the frequently occur-ring hazards in our cases are suggested below.

5.8. Other important control measures

Handling a variety of objects includingmaterials and tools, are asso-ciated with strains to various body parts. The injuries due to manualhandling can be minimized or eliminated by reducing loads or usingmechanical means.

The prevention of injuries and fatalities caused by interactionsbetween pedestrians and the machines are very effective. Dangersfrom machinery can arise in two main ways: firstly from machineryhazards including traps, impact, contact, entanglement and throughejection of materials such as oil or machine parts. Secondly, throughnon machinery hazards, which include electrical failure, exposure tochemicals, pressure, noise, vibration, radiation and high temperature.

Spontaneous combustion represents one of the major mining haz-ards adversely affecting human health and safety, economyand produc-tivity of mines. As one of the major disasters seriously affecting humanhealth, the high temperature fumes and poisonous gases produced byspontaneous combustion pose a serious threat to underground miners.Other suggestions are summarized as follows:

Fig. 8. Risk ranking in term

• Only qualified and experienced contractors should carry out theextraction of the mines.

• Develop a fire management risk reduction plan.• Provide training to all key personnel in the use of fire extinguishers.• Ensure that safe drinking water is available in the mine.• Keep fully supplied first aid boxes in the mine.• Provide adequate firefighting equipment around the mine.• Restrict access to all work areas to essential personnel only andexclude the general public.

• Ensure that all electrical installations are provided by suitablyqualified personnel.

• Facilitate access for operation, supervision and maintenance.• Eliminate illumination problems both in general lighting to themine and localized lighting for specific operations at stopes, andavoid glare.

• All machines should be kept in working order by regular maintenance.• Require the use of dust masks, gloves, safety glasses, safety footwear,face shields, goggles, etc.

• Provide continuous health controls.• Offer good insurance plans for the workers and their families.

The measures will have exclusively positive effects i.e. improvedsafety, but in some cases, the measures may produce both positiveand negative effects. Therefore depending on the situation the consis-tency of the measures should be checked. It is not of place to mentionthat an individual miner's obligation plays an important role in acci-dent/injury causation in mines.

Realistically, not all risks can be avoided in a coal mine and the de-tailed design should be developed on the basis of reducing risks to theALARP principle. In applying the ALARP principle, it will be determined

s of fuzzy numbers.

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what measures should be taken in advance of and during constructionto control risks.

Ongoing review of riskmanagement processes and controls is essen-tial to ensure that the management plan remains relevant. Factors thatmay affect the likelihood and consequences of an outcomemay changeover time, as they may affect the suitability or cost of the treatmentoptions. It is therefore necessary to monitor risks and their controls atregular intervals.

Mining is a dynamic and fast moving industry and the managementof change is an integral part ofmining activities. Howevermany changescan introduce new hazards into the workplace if not managed correctlyor can even invalidate risk assessments and control strategies. Modifica-tions and changes must be managed to ensure that human health andsafety risks arising from such changes remain at acceptable levels.

Finally, risk assessment should be seen as a continuing process andthe adequacy of control measures should also be subject to continualreview and revision if necessary.

6. Conclusion

A risk management methodology was carried out to providedecision-making support regarding choice of solutions and controlmeasures of human health and safety in three underground coalmines namely Hashouni, Hojedk and Babnizu located at the Kermancoal deposit.

For this purpose, fuzzy TOPSIS method which is the fuzzy extensionof TOPSIS technique was employed to analyze and assess the risk ofworking in the mines. The merit of this methodology as a logical, ratio-nal and computationally manageable approach is its avoidance of acomplex structure and/or a black box algorithm.

In our cases where a large number of different hazards and potentialincidents exist, altogether 86 events were identified and categorized ineight categories of geomechanical, geochemical, electrical, mechanical,chemical, environmental, personal, and social, cultural and managerialrisks.

According to the results, the events GM2, GM4, GM9, GM13, GC17,GC18, E20, M32, M34, EN48, S82 and S86 are major high-risk hazardsand need the most attention, while E25, M29, M30, EN46 and EN49are the least risky hazards in the investigated mines.

The high-risk hazards have led to a lot of casualties and major dam-age to miners and machinery, and unfortunately cannot be perfectlyavoided. The output of risk assessment helps to identify appropriatecontrols for reducing or eliminating these risks during the risk mitiga-tion process and can provide a basis for arriving at measures that canmodify the risk. The risk measures could be either likelihood or conse-quence reducing, depending on whether they apply to the right or tothe left side of the bow-tie diagram.

Generally, the strategy for risk management may be removal of thehazards or discontinuing the process. Where hazards and thereforerisks remain, then the residual risks have to be controlled or reducedby engineering and administrative controls. In addition, damage controland reactivity should be overtaken to understand thehazards before en-gineering changes to gain effective control by eliminating or effectivelymitigating the risks.

References

Allanson C. Strata control in underground coal mines: a risk management perspective. In:Aziz N, editor. Coal Operators' Conference, University of Wollongong & the Austral-asian Institute of Mining and Metallurgy; 2002. p. 135–53.

Applied Manual. Health and safety risk management manual for the Australian CoalMining Industry, Health and Safety Trust; 2007 [www.hstrust.com.au.].

Australian Mine Health Safety Regulationwww.legislation.nsw.gov.au, 2010.Aven T. Risk analysis: assessing uncertainties beyond expected values and probabilities.

John Wiley & Sons Ltd 978-0-470-51736-9; 2008.Aven T, Vinnem JE. Risk management, with applications from the offshore oil and gas

industry. New York, NY: Springer Verlag; 2007.Barnes M. Risk assessment workbook for mines. Metalliferous, extractive and opal mines,

and quarries. Mine Safety Operations; 2009. p. 64 [IGA-019 (TRIM: OUT09/16488)].

Buckley JJ. Fuzzy hierarchical analysis. Fuzzy Sets Syst 1985;17:233–47. http://dx.doi.org/10.1016/0165-0114(85)90090-9.

Bureau of Labor Statistics. U.S. Department of Labor; 2010 [http://www.bls.gov.].Chen CT. Extensions of the TOPSIS for group decision-making under fuzzy

environment. Fuzzy Set Syst 2000;114(1):1–9. http://dx.doi.org/10.1016/S0165-0114(97)00377-1.

Chen SJ, Hwang CL, Hwang FP. Fuzzy multiple attribute decision making. Lect Notes EconMath Syst 1992;375:1–531.

Coleman PJ, Kerkering JC. Measuring mining safety with injury statistics: lost workdays asindicators of risk. J Safety Res 2007;38(5):523–33. http://dx.doi.org/10.1016/j.jsr.2007.06.005.

Deb D. Analysis of coal mine roof fall rate using fuzzy reasoning techniques. Int J Rock MechMin Sci 2003;40(2):251–7. http://dx.doi.org/10.1016/S1365-1609(02)00133-8.

Duzgun HSB. Analysis of roof fall hazards and risk assessment for Zonguldak coal basinunderground mines. Int J Coal Geol 2005;64:104–15. http://dx.doi.org/10.1016/j.coal.2005.03.008.

Duzgun HSB, Einstein HH. Assessment and management of roof fall risks in undergroundcoal mines. Saf Sci 2004;42(1):23–41. http://dx.doi.org/10.1016/S0925-7535(02)00067-X.

Engineering report. Technical project for construction of the Babnizu mine, Iran; 1970. p.411 [Kharkov].

Fouladgar MM, Yadani-Chamzini A, Basiri MH. Risk evaluation of tunneling projects byfuzzy TOPSIS. International Conference onManagement (ICM 2011) Proceeding, Con-ference Master, Resources. No. 2011–087–331; 2011.

Göransson G, Norrman J, Larson M, Alén C, Rosén L. A methodology for estimating risksassociated with landslides of contaminated soil into rivers. Sci Total Environ 2014;472:481–95. http://dx.doi.org/10.1016/j.scitotenv.2013.11.013.

Grayson RL, Kinilakodi H, Kecojevic V. Pilot sample risk analysis for underground coalmine fires and explosions using MSHA citation data. Saf Sci 2009;47(10):1371–8.http://dx.doi.org/10.1016/j.ssci.2009.03.004.

Hwang CL, Yoon K. Multiple attribute decision making methods and applications. Berlin:Springer; 1981. http://dx.doi.org/10.1007/978-3-642-48318-9.

Iannacchione AT, Marshall TE, Prosser LJ. Failure characteristics of roof falls in an under-ground stone mine in southwestern Pennsylvania. Proc. of 20th Int. Conf. on GroundControl in Mining; 2001. p. 119–25. [Morgantown, WV].

ISO Guide. Risk management—vocabulary. International Organization for Standardization1sted. ; 2009. p. 73.

Joy J. Occupational safety risk management in Australian mining. Occup Med 2004;54:311–5. http://dx.doi.org/10.1093/occmed/kqh074.

Kaufmann A, Gupta MM. Introduction to fuzzy arithmetic: theory and applications. NewYork: Van Nostrand Reinhold Company Inc; 1985. p. 351.

Khanzode VV, Maiti J, Ray PK. A methodology for evaluation and monitoring of recurringhazards in underground coal mining. Saf Sci 2011;49:1172–9. http://dx.doi.org/10.1016/j.ssci.2011.03.009.

Kinilakodi H, Grayson RL. Citation-related reliability analysis for a pilot sample of under-ground coal mines. Accid Anal Prev 2011;43(3):1015–21. http://dx.doi.org/10.1016/j.aap.2010.11.033.

Kniesner TJ, Leeth JD. Data mining mining data: MSHA enforcement efforts, undergroundcoal mine safety, and new health policy implications. J Risk Uncertain 2004;29(2):83–111. http://dx.doi.org/10.1023/B:RISK.0000038939.25355.d8.

Kutlu AC, EkmekçiogluM. Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 2012;39:61–7. http://dx.doi.org/10.1016/j.eswa.2011.06.044.

Lai YJ, Liu TY, Hwang CL. TOPSIS for MODM. Eur J Oper Res 1994;76(3):486–500. http://dx.doi.org/10.1016/0377-2217(94)90282-8.

Lama RD, Bodziony J. Management of outburst in underground coal mines. Int J Coal Geol1998;35:83–115. http://dx.doi.org/10.1016/S0166-5162(97)00037-2.

Lee G, Jun KS, Chung ES. Integratedmulti-criteria flood vulnerability approach using fuzzyTOPSIS and Delphi technique. Nat Hazards Earth Syst Sci 2013;13:1293–312. http://dx.doi.org/10.5194/nhess-13-1293-2013.

MacNeill P. International mining fatality database. University of Wollongong; 2008[www.resources.nsw.gov.au].

Maiti J, Bhattacharjee A. Predicting accident susceptibility: a logistic regression analysis ofunderground coal mine workers. J S Afr Inst Metall 2001;101(4):203–8.

Maiti J, Bhattacherjee A. Evaluation of risk of occupational injuries among undergroundcoal mine workers through multinomial logit analysis. J Safety Res 1999;30(2):93–101. http://dx.doi.org/10.1016/S0022-4375(99)00003-1.

Maiti J, Khanzode VV. Development of a relative risk model for roof and side fall fatal ac-cidents in underground coal mines in India. Saf Sci 2009;47(8):1068–76. http://dx.doi.org/10.1016/j.ssci.2008.12.003.

Ministry of Cooperatives Labour, Social Welfare of Iran. Research center of human healthand safety. Classification of the safety status of coal mines in Kerman province; 2009.p. 48 [In Persian].

Mojtahedi SMH, Mousavi SM, Makui A. Project risk identification and assessment simul-taneously usingmulti-attribute group decisionmaking technique. Saf Sci 2010;48(4):499–507. http://dx.doi.org/10.1016/j.ssci.2009.12.016.

Paul PS. Predictors of work injury in underground mines—an application of a logistic re-gression model. Min Sci Technol 2009;19(3):282–9. http://dx.doi.org/10.1016/S1674-5264(09)60053-3.

Phillipson SE. The control of coal bed decollement-related slickensides on roof falls inNorth American Late Paleozoic coal basins. Int J Coal Geol 2003;53(3):181–95.http://dx.doi.org/10.1016/S0166-5162(02)00196-9.

PMBOK. A guide to the project management body of knowledge4th ed. ; 2008.Sari M, Duzgun HSB, Karpuz C, Selcuk AS. Accident analysis of two Turkish

underground coal mines. Saf Sci 2004;42(8):675–90. http://dx.doi.org/10.1016/j.ssci.2003.11.002.

Page 15: Human Health and Safety Risks Management in Underground Coal Mines Using Fuzzy TOPSIS

99S. Mahdevari et al. / Science of the Total Environment 488–489 (2014) 85–99

Sari M, Selcuk AS, Karpuz C, Duzgun HSB. Stochastic modeling of accident risks associatedwith an underground coal mine in Turkey. Saf Sci 2009;47:78–87. http://dx.doi.org/10.1016/j.ssci.2007.12.004.

Shahriar K, Bakhtavar E. Geotechnical risks in underground coal mines. J Appl Sci1812-5654 2009;9(11):2137–43.

SoltanPanah H, Farughi H, Heshami S. Ranking repair and maintenance projects of largebridges in Kurdestan province using fuzzy TOPSIS method. J Am Sci1545-10032011;7(7):227–33.

Statistical Energy Survey. BP statistical review of world energy; 2013 [www.bp.com].Wang YM, Elhag TMS. Fuzzy TOPSIS method based on alpha level sets with an application

to bridge risk assessment. Expert Syst Appl 2006;31(2):309–19. http://dx.doi.org/10.1016/j.eswa.2005.09.040.

Wang YJ, Lee HS. Generalizing TOPSIS for fuzzy multiple-criteria group decision-making.Comput Math Appl 2007;53:1762–72. http://dx.doi.org/10.1016/j.camwa.2006.08.037.

Wang YJ, Lee HS, Lin K. Fuzzy TOPSIS for multi-criteria decision making. Int Math J 2003;3:367–79.

Yazdani M, Alidoosti A, Basiri MH. Risk analysis for critical infrastructures using FuzzyTOPSIS. J Manag Res1941-899X 2012;4(1:E6). http://dx.doi.org/10.5296/jmr.v4i1.979.

Zadeh LA. Fuzzy sets. Inf Control 1965;8(3):338–53. http://dx.doi.org/10.1016/S0019-9958(65)90241-X.

Zadeh LA. The concept of a linguistic variable and its application to approximatereasoning-I. Inform Sci 1975;8:199–249.

Zhang S, Sun B, Yan L, Wang C. Risk identification on hydropower project using the IAHPand extension of TOPSIS methods under interval-valued fuzzy environment. NatHazards 2013;65:359–73. http://dx.doi.org/10.1007/s11069-012-0367-2.

Zhou X, Lu M. Risk evaluation of dynamic alliance based on Fuzzy analytic network pro-cess and Fuzzy TOPSIS. J Serv Sci Manag 2012;5:230–40. http://dx.doi.org/10.4236/jssm.2012.53028.

Zhu J, Xiao-ping M. Safety evaluation of human accidents in coal mine based on ant col-ony optimization and SVM. Procedia Earth Planet Sci 2009;1:1418–24. http://dx.doi.org/10.1016/j.proeps.2009.09.219.

Zimmermann HJ. Fuzzy set theory and its applications. Boston: Kluwer Academic Publisher;1992.