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Journal of Loss Prevention in the Process Industries 26 (2013) 197e208

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Journal of Loss Prevention in the Process Industries

journal homepage: www.elsevier .com/locate/ j lp

Developing a new fuzzy inference system for pipelinerisk assessment

Ali Jamshidi a, Abdolreza Yazdani-Chamzini b,*, Siamak Haji Yakhchali c,Sohrab Khaleghi d

aUniversity of Tehran, Tehran, Iranb Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran, IrancDepartment of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IrandHealth, Safety and Environment (HSE) Department, University of Tehran, Tehran, Iran

a r t i c l e i n f o

Article history:Received 3 June 2012Received in revised form25 October 2012Accepted 25 October 2012

Keywords:Risk assessmentFuzzy logicPipelineMamdani algorithm

* Corresponding author.E-mail addresses: [email protected] (A.

gmail.com (A. Yazdani-Chamzini), yakhchali@[email protected] (S. Khaleghi).

0950-4230/$ e see front matter Crown Copyright � 2http://dx.doi.org/10.1016/j.jlp.2012.10.010

a b s t r a c t

The problem of less and/or even lack of information and uncertainty in modeling and decision makingplays a key role in many engineering problems; so that, it results in designers and engineers could notreach to sure solutions for the problems under consideration. In this paper, an application of the fuzzylogic for modeling the uncertainty involved in the problem of pipeline risk assessment is developed. Forachieving the aim, relative risk score (RRS) methodology, one of the most popular techniques in pipelinerisk assessment, is integrated with fuzzy logic. The proposed model is performed on fuzzy logic toolboxof MATLAB� using Mamdani algorithm based on experts’ knowledge. A typical case study is imple-mented and a comparison between the classical risk assessment approach and the proposed model ismade. The results demonstrate that the proposed model provides more accurate, precise, sure results; sothat, it can be taken into account as an intelligent risk assessment tool in different engineering problems.

Crown Copyright � 2012 Published by Elsevier Ltd. All rights reserved.

1. Introduction technique requires that can assess the existing risks more precise,

Transport of flammable substances (including liquid and gasmaterials) in pipelines plays a key role in different branches ofmanufacturing and industry. Pipelines are generally recognized tobe the safest and most economical ways of transporting flammablesubstances in comparison with other methods of transport, such asroad and rail (Markowski & Mannan, 2009). A case in point, most ofoil and gas are transported in pipelines. Because oil and gastransmission pipelines are mainly installed underground, there aremany deteriorated factors affecting them and the main causesinclude corrosion, interference from the third party, materialdefects, malfunction, and natural hazard (Yuhua & Datao, 2005).The incapacitation or destruction of such infrastructures wouldhave a debilitating impact on national security and economic,public health or safety, or any combination of those matters (NIPP,2009). Therefore, risk assessment can help authorities to determineriskier components and make an appropriate reaction and strategyin order reduce and/or even omit it. For achieving the aim, a proper

Jamshidi), [email protected] (S.H. Yakhchali),

012 Published by Elsevier Ltd. All

accurate, and sure.According to the importance of pipelines, several researches are

conducted to analyze risks connected with pipelines. Dziubinski,Fratczaka, and Markowski (2006) presented a methodology of riskassessment for hazards associated with transportation of dangeroussubstances in long pipelines. Han and Weng (2010) proposed anintegrated quantitative risk analysis method for natural gas pipelinenetwork. The method is composed of the probability assessment ofaccidents, the analysis of consequences and the evaluation of risk.

Brito, Almeida, and Mota (2010) proposed a multicriteria modelbased on the ELECTRE TRI method and Utility Theory for assessingrisk in natural gas pipelines and for classifying sections of pipelineinto risk categories. Jo and Ahn (2005) developed a simplifiedmethod for the quantitative risk assessment for natural gas pipe-lines and introduced parameters of fatal length and cumulative fatallength. An application of self-organizing maps (SOMs) to assess therisk of third-party interference and classify their risk patterns isproposedbyLiang,Hu, Zhang,Guo, and Lin (2012). In this study, faulttree is used first to establish the risk assessment index system, andthen SOM is used in multi-parameter risk pattern classificationapproach. Cagno, Caron, Mancini, and Ruggeri (2000) proposeda robust Bayesian approach to support the replacement policy oflow-pressure cast-iron pipelines used in metropolitan gas distri-bution networks by the assessment of their probability of failure.

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Table 1Several applications of fuzzy logic in modeling risk.

Proposed by Application

Sadiq and Husain (2005) Environmental risk assessment of drillingwaste

Sadiq, Kleiner, and Rajani (2007) Water quality failures in distributionnetworks

Azadeh, Fam, Khoshnoud,and Nikafrouz (2008)

Performance assessment of health, safety,environment (HSE) and ergonomicssystem factors in a gas refinery.

Markowski and Mannan (2008) Distillation column unit

Elsayed (2009)Liquefied natural gas LNG shiploading/offloading at the terminal

Feng and Luo (2009) Landfall typhoonFlores, Mombello, Jardini,

and Rattá (2009)Power transformer failures

Grassi, Gamberini, Mora,and Rimini (2009)

Risk evaluation in workplaces

Gürcanli and Müngen (2009) Occupational safety risk analysis atconstruction sites

Markowski and Mannan (2009) Piping risk assessmentMarkowski, Mannan, and

Bigoszewska (2009)Process safety analysis

Rehana and Mujumdar (2009) Water quality managementAcosta, Wu, and Forrest (2010) Marine biosecurity managementBajpai, Sachdeva, and

Gupta (2010)Security risk assessment in chemicalprocess industries

Nieto-Morote and Ruz-Vila(2011)

Construction project risk assessment

Yazdani, Alidoosti, andZavadskas (2011)

Critical infrastructures

Alidoosti, Yazdani, Fouladgar,and Basiri (2012)

Critical infrastructures

Fouladgar, Yazdani-Chamzini,and Zavadskas (2012)

Tunneling projects

RRS

LIF

PH

LV

DI

RE

IS

IODCTPD

Fig. 1. RRS model for pipeline risk assessment.

A. Jamshidi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 197e208198

Shahriar, Sadiq, and Tesfamariam (2012) employed fuzzy logic toderive fuzzy probabilities (likelihood) of basic events in fault treeand to estimate fuzzy probabilities (likelihood) of output eventconsequences. The study also explores how inter-dependenciesamong various factors might influence analysis results. Teixeira,Soares, Netto, and Estefen (2008) assessed the reliability of pipe-lines with corrosion defects subjected to internal pressure using thefirst-order reliability method (FORM). Amirat, Mohamed-Chateauneuf, and Chaoui (2006) conducted a reliability assess-ment for underground pipelines under the combined effect of activecorrosion and residual stress. Soszynska (2010) applied the multi-state approach to the analysis and evaluation of systems’ reli-ability and risk. Bajcar, Sirok, Cimerman, and Eberlinc (2008) pre-sented a model for the assessment of the influence of line markerson risk on transmission pipelines with natural gas. Pandey (1998)presented a probabilistic analysis framework to estimate the pipe-line reliability incorporating the impact of inspection and repairactivities planned over the service life. Crawley, Lines, and Mather(2003) investigated the key parameters for risk assessment near tooil/gas pipelines with particular reference to flames. Seleznev andAleshin (2006) conducted a numerical analysis of fire risk at pipe-line systems of industrial power facilities. A decision model for riskassessment and for risk ranking of sections of natural gas pipelinesbased on multi-attribute utility theory is presented by Brito and deAlmeida (2009). A comparison study on qualitative and quantitativerisk assessment methods for urban natural gas pipeline network isaccomplished by Han and Weng (2011). Gharabagh et al. (2009)developed an algorithm using probabilistic and indexing models toovercome most of the limitations of the models, which is an appro-priate technique for the comprehensive risk assessment andmanagement of pipelines. Breton, Sanchez-Gheno, Alamilla, andAlvarez-Ramirez (2010) proposed aBayesianprobabilistic approach toidentify and predict the type of failure (leakage or rupture) for steelpipelines under realistic corroding conditions.Meresht, Farahani, andNeshati (2011) developeda failure analysis of stress corrosion crackingoccurred in a gas transmission steel pipeline. Lecchi (2011) carried outaprobabilistic assessmentof corrodedpipelines inspectedwith InLineInspection (ILI) tools. Yuhua and Datao (2005) combined expert elic-itation with fuzzy set theories to evaluate probability of the events.

Often above-mentioned studies only applied two main parame-ters likelihood and consequences in order to assess the level of risksinvolved in pipelines; while, these two factors cannot cover allaspects of the risks involved in pipelines. On the other hand, uncer-tainty is an inseparable part of real world systems; so that, Booleanlogic is not able to handle this inherent uncertainty and complexity.The existing uncertainty is created by two main parts (Fouladgar,Yazdani-Chamzini, & Zavadskas, 2011): (1) uncertainty in subjectivejudgments (2) uncertainty due to lack of data or incomplete infor-mation. Fuzzy logic is a powerful tool for facingwith uncertainty andsolves problems where there are no sharp boundaries and precisevalues. Thismethod isused to solvedifferent aspectsof riskproblems.Table 1 lists several applications of fuzzy logic in modeling risk.

From Table 1, it can be seen that fuzzy logic has demonstrated itscapabilities and efficiencies as a practical engineering and problem-solving tool.

The main objective of the paper is to present a new method-ology based on relative risk score (RRS) and fuzzy inference systemfor providing a systematic framework in order to make a more sure,precise, and robust model for controlling the risks and hazardsassociated with pipelines. The main vision of the plan is to ensurethat the pipeline under consideration is resilient, safe, and able toquickly detect failures, and as a result reduce the adverse conse-quences. To show the capability and effectiveness of the proposedmodel, the results derived from the proposed model are comparedwith the outputs obtained by the conventional method.

The rest of the paper is organized as follows. Next sectiondescribes the basic structure of traditional relative risk score. InSection 3, Fuzzy inference system is briefly illustrated, includingfuzzy theory, fuzzification, knowledge base, fuzzy inference system,and defuzzification. The proposed model is introduced in Section 4.The implementation of the proposed model is presented in Section5. A real case study is illustrated in Section 6. Comparison betweenthe proposedmodel and traditional RRS is summarized in Section 7.In the last section, conclusions are discussed.

2. Traditional RRS framework

Relative risk score (RRS) is a logical tool for evaluating riskconnected with pipelines based on failure of pipeline system. Thismodel is applied not only for its comprehensive risk assessment,but also to obtain comprehensive information for inspection,maintenance, modification, or risk management of the petro-chemical feed and product pipelines (Gharabagh et al., 2009). Thistechnique has been recently used by different researchers to ensuresafety and reliability of pipelines.

Fig. 2. Fuzzy inference systems.

A. Jamshidi et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 197e208 199

The RRS model indices, introduced by Muhlbauer (2004), are anexhaustive reference on determining the effective factors forassessing the pipeline risk. The indices of pipeline failure employedby this research contain of eight parameters, including third-partydamage (TPD), corrosion (C), design (D), incorrect operation (IO),product hazard (PH), leak volume (LV), dispersion (DI), and recep-tors (RE). These eight criteria are grouped into two main criteriaindex sum and leak impact factor as depicted in Fig. 1.

Based on basic concepts of the traditional RRS technique, rela-tive risk score (RRS) is calculated by the intersection of index sum(IS) and leak impact factor (LIF). More specifically, risk is formulatedas following equations (Muhlbauer, 2004):

RSS ¼ ISLIF

RSS ¼ Score of ISScore of LIF

(1)

where:

Fig. 3. A typical Mamdani inference mec

IS ¼ TPDþ Cþ Dþ IO (2)

LIF ¼ PH� LV� DI� RE (3)

3. Fuzzy inference system

In many practical problems, the evaluators face with lack or lessof information or incomplete data in modeling real worldphenomena; so that, vagueness and uncertainty are inseparableaspects of knowledge. Fuzzy logic, introduced by Zadeh (1965), isa powerful tool to face with such situations. This technique applieslinguistic terms to provide an inference structure for modelingsophisticated and complex structures. A fuzzy set is general form ofa crisp set that belong to the closed interval 0 and 1; so that, 1addresses full membership and 0 expresses non-membership(Yazdani-Chamzini, Yakhchali, 2012). Whereas, crisp sets onlyallow 0 or 1.

A typical fuzzy inference system (FIS) is schematically depictedin Fig. 2. As shown in Fig. 2, an FIS includes four main parts (1)fuzzification, (2) Knowledge base, (3) fuzzy inference system, and(4) defuzzification.

3.1. Fuzzification

The fuzzification comprises the process of transforming crispvalues into grades of membership for linguistic terms of fuzzy sets.The membership function is used to associate a grade to eachlinguistic term.

The first part in FIS is fuzzification in which the process oftransferring crisp values into fuzzy IfeThen rules is implemented

hanism (Mamdani & Assilian, 1975).

Fig. 4. Schematic diagram of the proposed model.



through grades of membership for linguistic variables of fuzzy sets.In other words, input vector (crisp values) may be translated intolinguistic terms, such as very high, high, medium, low, and verylow. This process is fulfilled with the help of membership function(MF). These functions have different types of linear and nonlinearshapes. The type of the MF depends on the modeled problem,experts’ knowledge and contexts (Grima, Bruines, & Verhoef,2000).

3.2. Knowledge base

From Fig. 1, it can be seen that database and rule base formknowledge base, which the MFs of the fuzzy sets applied ingenerating the fuzzy rules are defined by the database and fuzzy ifethen rules build the rule base. The fuzzy ifethen rules are extractedfrom experts’ judgments, engineering knowledge and experience(Ghasemi & Ataei, 2012).

The inputeoutput relationships are defined by fuzzy conditionalfunctions that are known as fuzzy “ifethen” rules. A fuzzy condi-tional rule is generally made up of a premise (antecedent) anda consequent (conclusion) part for example “if x is high (premise)then y is low (consequent)” where the terms high and low can berepresented by MFs (Jang, Sun, & Mizutani, 1997).

3.3. Fuzzy inference system

In this step, the fuzzy inference unit uses these fuzzy IfeThenrules to assign a map from fuzzy inputs to fuzzy outputs based onfuzzy composition rules (Li, 2006). This step is the main part ofa fuzzy expert system that aggregates the facts derived from thefuzzification process with the rule base generated in previous partand conducts the modeling process.

There are several FISs that have been applied in different aspectsof science and engineering applications. Mamdani fuzzy model isone of the most popular algorithms. This method uses the conceptsof fuzzy sets and fuzzy logic to translate an entirely unstructuredset of linguistic heuristics into an algorithm (Mamdani & Assilian,1975). The general “ifethen” rule form of the Mamdani algorithm(seen in Fig. 3) is given in the following:

If x1 is Ai1 and x2 is Ai2 and.xr is Air then y is Biðfor i¼ 1;2;.kÞ (4)

where xi is the input variable, Airand Biare linguistic terms,y is theoutput variable, and kis the number of rules.

Fig. 5. Structure of th

Different fuzzy compositionmethods can be applied to establishtheMamdani fuzzymodel. In this paper,maxemin composition, themost commonly used method (Ross, 2010), is utilized. This tech-nique ismathematically defined as follows (Monjezi & Rezae, 2011):

mCKðZÞ ¼ max

�min

�mAK

ðinputðxÞÞ;mBKðinputðyÞÞ�� K

¼ 1;2; :::; r (5)

where mCK, mAK

, and mBKare the membership functions of output “z”

for rule “k”, input “x”, and “y”, respectively.

3.4. Defuzzification

Finally, the defuzzification process is used to transfer fuzzy setsinto crisp value. There are several defuzzifier methods in the litera-ture. Centroid of area (COA) is one of the most popular methods fordefuzzification process. The advantage of the COA method is that allactivated membership functions of the conclusions (all active rules)take part in the defuzzification process (Daftaribesheli, Ataei, &Sereshki, 2011). The COA method applies the following equation fortransferring fuzzy scheme into a crisp value (Iphar & Goktan, 2006):

Z*COA ¼

Zz

mAðzÞzdzZz

mAðzÞdz(6)

where Z*COA is the crisp value for the “z” output and mA(z)is theaggregated output membership function.

4. The proposed model for fuzzy risk assessment of pipelines

The fuzzy risk analysis method proposed in this study contains ofthree phases the IS assessment, the LIF evaluation, and the riskanalysis, which the first two phases are formed based on concepts offuzzy logic. Fuzzy logic is applied to handle the uncertainty involvedin theprocess ofmodeling. A specific feature of theproposedmodel isan integratedmodel based on qualitative andquantitative techniquesfor pipeline risk assessment. This can result in a complete and moreaccurate assessment of risks connected with hazard sources. Theframeworkof theproposedmethod is schematicallydepicted inFig. 4.

The first phase focuses on the overall failure probability, whichis caused by third-party damage, corrosion, design, and incorrectoperation. This phase calculates the potential for a particular failuremechanism to be active and is subtly different from the likelihood

e fuzzy IS model.

Table 2Definition of fuzzy and crisp ratings.

Factors Linguisticterm

Crisprating

Fuzzy ratings Universe ofdiscourse (X)

TDP Very high (VH) 5 3.5 < TDP � 5 XTDP V (1,5)High (H) 4 3 � TDP < 5Medium (M) 3 2 � TDP � 4Low (L) 2 1 � TDP � 3Very low (VL) 1 1 � TDP < 2.5

C Very high (VH) 5 3.5 < C � 5 XC V (1,5)High (H) 4 3 � C < 5Medium (M) 3 2 � C � 4Low (L) 2 1 � C � 3Very low (VL) 1 1 � C < 2.5

D Very high (VH) 5 3.5 < D � 5 XD V (1,5)High (H) 4 3 � D < 5Medium (M) 3 2 � D � 4Low (L) 2 1 � D � 3Very low (VL) 1 1 � D < 2.5

IO Very high (VH) 5 3.5 < IO � 5 XIO V (1,5)High (H) 4 3 � IO < 5Medium (M) 3 2 � IO � 4Low (L) 2 1 � IO � 3Very low (VL) 1 1 � IO < 2.5

IS Very high (VH) 5 (18e20)a 3.5 � IS � 5 XIS(fuzzy) V (0,5),XIS(crisp) V (1,5),High (H) 4 (14e17)a 2.5 � IS < 4.5

Medium (M) 3 (10e13)a 1.5 � IS � 3.5Low (L) 2 (6e9)a 0.5 � IS � 2.5Very low (VL) 1 (1e5)a 0 � IS � 1.5

PH Very high (VH) 5 3.5 < PH � 5 XPH V (1,5)High (H) 4 3 � PH < 5Medium (M) 3 2 � PH � 4Low (L) 2 1 � PH � 3Very low (VL) 1 1 � PH < 2.5

LV Very high (VH) 5 3.5 < LV � 5 XLV V (1,5)High (H) 4 3 � LV < 5Medium (M) 3 2 � LV � 4Low (L) 2 1 � LV � 3Very low (VL) 1 1 � LV < 2.5

DI Very high (VH) 5 3.5 < DI � 5 XDI V (1,5)High (H) 4 3 � DI < 5Medium (M) 3 2 � DI � 4Low (L) 2 1 � DI � 3Very low (VL) 1 1 � DI < 2.5

RE Very high (VH) 5 3.5 < RE � 5 XRE V (1,5)High (H) 4 3 � RE < 5Medium (M) 3 2 � RE � 4Low (L) 2 1 � RE � 3Very low (VL) 1 1 � RE < 2.5

LIF Very high (VH) 5 (399e625)b 3.5 < LIF�5 XLIF(fuzzy) V (0,5),XLIF(crisp) V (1,5),High (H) 4 (144e399)b 2.5 � LIF�4.5

Medium (M) 3 (36e143)b 1.5 � LIF�3.5Low (L) 2 (5e35)b 0.5 � LIF�2.5Very low (VL) 1 (1e4)b 0 � LIF<1.5

a The IS value resulted through Eq. (2).b The LIF value obtained by Eq. (3).


of failure (Muhlbauer, 2004). By using the first FIS model estab-lished in MATLAB� software package, the IS assessment can becalculated as the result of the overall failure probability.

The second phase concentrates on the overall consequence ofa pipeline failure, including product hazard, leak volume, disper-sion, and receptors. In this phase, similar to the previous phase, byapplying the second FIS model established in MATLAB� softwarepackage, the LIF evaluation is derived from the overall potentialconsequences of pipeline failure.

The last phase computes the final risk score to evaluate the levelof risk in order to determine the proper mitigation strategy foractivity continuity. In the first step of this phase, the index sumresulted from the first phase is combined with the leak impactfactor derived from the second phase to arrive at a risk value foreach pipeline section. After computing the risk values, these valuesare ranked in descending order. In the last step of the phase, riskiersections are highlighted to be improved by appropriate strategies.

5. Modeling fuzzy risk assessment

The procedure of the proposed model is presented by a stepwiseprocess in the following part.

Phase 1. Pipeline failure probability refers to the potential for anexacting failure mechanism to be happened, and is induced by theinfluence of internal parameters on the IS value. In this study, FISnetwork is established for calculating the IS values or the overallfailure probability. The fuzzy model based on Mamdani algorithm isimplemented on fuzzy logic toolbox of MATLAB� software package.In order to composite fuzzy relations, the maxemin compositionmethod, most popular applied technique (Ross, 2010), is selected.Input and output variables in the MATLAB� environment are depic-ted in Fig. 5. These variables are fuzzifiedwithmembership functionspresented inTable2as shown inFigs. 6 and7.As seen in thefigure, theGaussian type of membership functions are employed because ofbeing the most natural (Markowski & Mannan, 2008), smooth andnonzero at all points (Xie, 2003). The Gaussianmembership functionis based on two parameters and can be represented by Eq. (7):

Gaussionðx; c; sÞ ¼ e�1

2

�x� cs

�2

(7)

where c and s are the center and width of the membership func-tion, respectively. The authors adjusted parameter sso that everymembership function has 50 percent overlapping. This causes therisk of introducing a “hole” in the input domain be eliminated (Janget al., 1997). Based on the basic descriptions of Mamdani model,both input and output variables are fuzzy propositions.

The next step is the construction of the fuzzy ifethen rules.These rules represent the fuzzy relations between input and outputvariables. Based on experts’ knowledge, the rule base of the fuzzymodel is constructed. A sample of the fuzzy ifethen rules including21 rules of the model established in MATLAB� software package islisted in Fig. 8.

In the last step, the defuzzification process is applied to fuzzyvalues be converted into a crisp ones. In this paper, the COAmethod, one of the most common methods, is employed fordefuzzification process.

The interdependency of input and output parameters derivedfrom the rules generated in the fuzzy ISmodel can be shownby usingcontrol surface as depicted in Fig. 9. As seen in the figure, Fig. 9(i)shows the interdependency of IS on IO and C, Fig. 9(ii) depictsinterdependency of IS on IO and TDP, and Fig. 9(iii) shows interde-pendency of IS on IO and D. From the figure, it can clearly be resultedthat variation of output with input is found to be in an agreementwith literature.

Phase 2. In this phase, the overall consequence of a pipelinefailure, called as LIF model, is established in MATLAB� softwarepackage. The consequence of pipeline failure depends on the char-acteristics of the substance carried by the transmission pipelinesand the environment around the pipelines (Han & Weng, 2010).

Similar to the first phase, the maxemin composition is selectedto composite the fuzzy rules. The inputeoutput structure of the FISmodel established in this phase is presented in Fig. 10. Input vari-ables and output variable applied in modeling are fuzzified withGaussian membership function based on Table 2 as depicts inFigs. 11 and 12.

After constructing the initial structure of the FIS model, the rulebase is formed based on expert’s opinion. A sample of the fuzzyrules including 14 ifethen rules generated by expert team is listedin the following:

Fig. 6. Membership functions of input variables involved in the IS model; (i) TDP, (ii) C, (iii) D, and (iv) IO.

Fig. 7. Membership function of the output (IS) variable.


1. If (PH is VL) and (LV is VL) and (DI is VL) and (RE is VL) then (LIFis VL) (1)

2. If (PH is L) and (LV is L) and (DI is L) and (RE is L) then (LIF isL) (1)

Fig. 8. Graphical indication of fu

3. If (PH is M) and (LV is M) and (DI is M) and (RE is M) then (LIF isM) (1)

4. If (PH is H) and (LV is H) and (DI is H) and (RE is H) then (LIF isH) (1)

zzy reasoning mechanism.

Fig. 9. Control surface of IS on (i) IO and C; (ii) IO and TDP, and (iii) IO and D.


5. If (PH is VH) and (LV is VH) and (DI is VH) and (RE is VH) then(LIF is VH) (1)

6. If (PH is L) and (LV is M) and (DI is M) and (RE is M) then (LIF isM) (1)

7. If (PH is H) and (LV is H) and (DI is VH) and (RE is H) then (LIF isH) (1)

8. If (PH is VL) and (LV is L) and (DI is M) and (RE is H) then (LIF isM) (1)

9. If (PH is VH) and (LV is H) and (DI is M) and (RE is VH) then (LIFis H) (1)

10. If (PH is VH) and (LV is VH) and (DI is VH) and (RE is H) then (LIFis VH) (1)

Fig. 10. Structure of fuzzy LIF model.

Fig. 11. Membership functions of input variables involved in the LIF model; (i) PH, (ii) LV, (iii) DI, and (iv) RE.

Fig. 12. Membership functions of the output (LIF) variable.


11. If (PH is VH) and (LV is VH) and (DI is L) and (RE is L) then (LIF isH) (1)

12. If (PH is H) and (LV is H) and (DI is L) and (RE is L) then (LIF isM) (1)

13. If (PH is L) and (LV is M) and (DI is H) and (RE is H) then (LIF isH) (1)

14. If (PH is M) and (LV is H) and (DI is M) and (RE is VH) then (LIF isH) (1)

Fig. 13. Control surface of LIF on (i) RE and DI; (ii) RE and PH, and (iii) RE and LV.


In the last step of this phase, by using the COA method, defuz-zification process is performed for obtaining the crisp values. Theinterdependency of input and output parameters derived from therules generated in the fuzzy LIF model can be shown by usingcontrol surface as depicted in Fig. 13. This figure shows the influ-ence of input parameters on the output parameter in the fuzzy

model by graphical representation for visual perception. Accordingto the figure, variation of output with input is in an agreement withliterature. This indicates the excellent identification capability ofthe fuzzy model.

Phase 3. In the last phase, risk index of pipeline failure is calcu-lated through a process ofmeasure that is defined as amathematical

Table 4Risk assessment of 5 pipeline sections.

Section Traditional RRS Proposed model

IS Rank LIF Rank RRS Rank IS Rank LIF Rank RRS Rank

1 11 1 12 1 0.917 3 2.89 1 2.12 1 1.363 42 12 4 120 4 0.100 2 3.21 4 3.67 5 0.875 13 11 1 120 4 0.092 1 2.93 2 3.34 4 0.877 24 11 1 12 1 0.917 3 3.05 3 2.14 2 1.425 55 12 4 24 3 0.500 5 3.26 5 2.87 3 1.136 3


function of the IS and LIF values resulted by the first and secondphases, respectively. Therefore, by using the relation presented inEq. (5), the RRS values are calculated.

6. Case study

A numerical case study as an illustration of the potentialapplication of the proposed model for pipeline risk assessment ispresented. This application is based on information taken froma natural gas pipeline (Farzam, Keivanloo, & Nikrooz, 2007, pp. 1e14). The total length of this pipeline is approximately 54.6 km.According to risks are not constant along a pipeline, it is necessaryto first divide the line into manageable sections with constant riskfeatures. Therefore, this line applies a two-stage process. In the firststage that is about 14.1 km, the pipeline transports gas from gasgeneration stations located in the south of Ahwaz town to Sepantastation situated in the west of Ahwaz town. This stage contains oftwo sections 9 and 5.1 km. The second stage contains a length ofabout 40.5 km that connects the south of Dezful city with townborder station (TBS) No. 2 at the end of the path. This stagecomprises three sections 15, 15, and 10.5 km. The analysis is per-formed for all of five section located close to a township. Theprocess of risk assessment is performed by using the proposedmodel for ranking five pipeline sections according to the RRS valuesand the section with the lowest score is selected as the riskiestsection. This process helps authorities to take into account thesuitable strategies in order to reduce or mitigate the levels of risk ofeach section. The results of the proposed model for risk assessmentof 5 sections based on Eqs. (1)e(3) are calculated. In order to betterunderstand the matter, an example is presented as follows:

ISSection1 ¼ 2þ 4þ 2þ 3 ¼ 11

LIFSection1 ¼ 3� 2� 2� 1 ¼ 12

As a result,

RRSSection1 ¼ ISSection1LIFSection1

¼ 1112

¼ 0:917

Similar calculations are accomplished for others sections andthe results are summarized in Table 3. As seen in the last column ofTable 3, the sections are ranked by decreasing values of RRS asfollows:

Section2_Section3_Section5_Section1_Section4

7. Comparison with traditional RRS

In order to compare the results obtained by the proposed modeland traditional RRS, the outputs from twomethods are presented inTable 4. The main drawback of the traditional RRS is that differentdatasets of input variables (i.e. TPD, C, D, and IO or PH, LV, DI, and RE)may generate a same value of IS and LIF, and consequently a similarvalue of risk index.However, the risk implicationmaynotessentiallybe the same. For instance, three sections 1, 3, and 4 have an IS value

Table 3Output of the proposed model.

Section TPD C D IO PH LV DI RE IS LIF RRS

1 2 4 2 3 3 2 2 1 2.89 2.12 1.3632 3 2 2 5 5 3 4 2 3.21 3.67 0.8753 1 1 5 4 2 4 5 3 2.93 3.34 0.8774 2 5 1 3 4 3 1 1 3.05 2.14 1.4255 2 5 3 2 1 2 3 4 3.26 2.87 1.136

of 11;whereas, theyhave values of (2,4,2,3), (1,1,5,4), and (2,5,1,3) forTPD, C, D, and IO, respectively. Likewise, two sections 1 and4haveanLIF value of 12; while, the values of (3,2,2,1) and (4,3,1,1) for PH, LV,DI, and RE, respectively, are considered. However, the risk implica-tions of the sections may be significantly different. This problemmay impose a waste of time and finance.

Another limitation of the traditional RRS is that it cannot takeinto account the relative importance among input variables. Asa result, the outputs of the traditional RRS may be inaccurate inreal-life problems. Whereas, the proposed model takes intoaccount the relative importance among the variables. Therefore,the output of the proposed model for pipeline risk assessment ismore sure, precise, and accurate.

8. Conclusions

In this paper, an integrated methodology based on fuzzy logicand relative risk score (RRS) for hazards connected with pipelines isproposed. A specific feature of the proposedmodel is a combinationof qualitative (RRS) and quantitative (fuzzy inference system)techniques. The merit of using fuzzy logic is to handle often asso-ciated with RRS components (including third-party damage,corrosion, design, incorrect operation, product hazard, leak volume,dispersion, and receptors).

To demonstrate the potential applications of the proposedmodelfor assessing the level of risk in pipelines, a real world case study isillustrated and the results are compared with the results of theconventional method. For achieving the aim, the pipeline trans-porting gas from gas generation stations located in the south ofAhwaz town toSepanta station situated in thewestof Ahwaz town isadopted. This pipeline contains five sections and the risk of eachsection is assessed by using both the proposed model and theconventional method. The results show that the proposed model isbetter than the conventional method. This improves the possibilityof a complete risk assessment for pipelines. Therefore, it helps toassign riskier items in order to allocate the limited time andresources. The results demonstrate that the proposed model iscapable to remove themain shortcomings of the traditional RRS. Theadvantages of the proposedmodel are, but not limited to, as follows:

(1) The output of the proposed model is more accurate, precise,and sure than the traditional RRS.

(2) The relation between input and output information in thefuzzy proposed system is described as linguistic variables,which are more flexible and realistic in reflecting realsituations.

(3) In contrast with the traditional RRS, the proposedmodel is ableto take into account the relative importance among theparameters influenced on risk index.

Acknowledgment

The authors would like to acknowledge the financial support ofUniversity of Tehran for this research.


References

Acosta, H., Wu, D., & Forrest, B. M. (2010). Fuzzy experts on recreational vessels, a riskmodelling approach for marine invasions. Ecological Modelling, 221, 850e863.

Alidoosti, A., Yazdani, M., Fouladgar, M. M., & Basiri, M. H. (2012). Risk assessment ofcritical asset using fuzzy inference system. Risk Management, 14, 77e91. http://dx.doi.org/10.1057/rm.2011.19.

Amirat, A., Mohamed-Chateauneuf, A., & Chaoui, K. (2006). Reliability assessment ofunderground pipelines under the combined effect of active corrosion andresidual stress. International Journal of Pressure Vessels and Piping, 83, 107e117.

Azadeh, A., Fam, I. M., Khoshnoud, M., & Nikafrouz, M. (2008). Design and imple-mentation of a fuzzy expert system for performance assessment of an inte-grated health, safety, environment (HSE) and ergonomics system: the case ofa gas refinery. Information Sciences, 178, 4280e4300.

Bajcar, T., Sirok, B., Cimerman, F., & Eberlinc, M. (2008). Quantification of impact ofline markers on risk on transmission pipelines with natural gas. Journal of LossPrevention in the Process Industries, 21, 613e619.

Bajpai, S., Sachdeva, A., & Gupta, J. P. (2010). Security risk assessment: applying theconcepts of fuzzy logic. Journal of Hazardous Materials, 173, 258e264.

Breton, T., Sanchez-Gheno, J. C., Alamilla, J. L., & Alvarez-Ramirez, J. (2010). Identi-fication of failure type in corroded pipelines: a bayesian probabilistic approach.Journal of Hazardous Materials, 179, 628e634.

Brito, A. J., & de Almeida, A. T. (2009). Multi-attribute risk assessment for riskranking of natural gas pipelines. Reliability Engineering and System Safety, 94,187e198.

Brito, A. J., Almeida, A. T., & Mota, C. M. M. (2010). A multicriteria model for risksorting of natural gas pipelines based on ELECTRE TRI integrating Utility Theory.European Journal of Operational Research, 200, 812e821.

Cagno, E., Caron, F., Mancini, M., & Ruggeri, F. (2000). Using AHP in determining theprior distributions on gas pipeline failures in a robust bayesian approach.Reliability Engineering and System Safety, 67, 275e284.

Crawley, F. K., Lines, I. G., & Mather, J. (2003). Oil and gas pipeline failure modelling.Trans IChemE, 81, 3e11.

Daftaribesheli, A., Ataei, M., & Sereshki, F. (2011). Assessment of rock slope stabilityusing the Fuzzy Slope Mass Rating (FSMR) system. Applied Soft Computing, 11,4465e4473.

Dziubinski, M., Fratczaka, M., & Markowski, A. S. (2006). Aspects of risk analysisassociated with major failures of fuel pipelines. Journal of Loss Prevention in theProcess Industries, 19, 399e408.

Elsayed, T. (2009). Fuzzy inference system for the risk assessment of liquefiednatural gas carriers during loading/offloading at terminals. Applied OceanResearch, 31, 179e185.

Farzam, M., Keivanloo, K., Nikrooz, B. (2007). Risk assessment of gas transportationpipeline from the south of Ahwaz to the south of Dezful. The first internationalconference on technical inspection and NDT, Tehran, Iran (in Persian).

Feng, L. H., & Luo, G. Y. (2009). Analysis on fuzzy risk of landfall typhoon inZhejiang province of China. Mathematics and Computers in Simulation, 79,3258e3266.

Flores, W. C., Mombello, E., Jardini, J. A., & Rattá, G. (2009). Fuzzy risk index forpower transformer failures due to external short-circuits. Electric Power SystemsResearch, 79, 539e549.

Fouladgar, M. M., Yazdani-Chamzini, A., & Zavadskas, E. K. (2011). An integratedmodel for prioritizing strategies of the Iranian mining sector. Technological andEconomic Development of Economy, 17(3), 459e483.

Fouladgar, M. M., Yazdani-Chamzini, A., & Zavadskas, E. K. (2012). Risk evaluation oftunneling projects. Archives of Civil and Mechanical Engineering, 12, 1e12.

Gharabagh, M. J., Asilian, H., Mortasavi, S. B., Mogaddam, A. Z., Hajizadeh, E., &Khavanin, A. (2009). Comprehensive risk assessment and management ofpetrochemical feed and product transportation pipelines. Journal of LossPrevention in the Process Industries, 22, 533e539.

Ghasemi, E., & Ataei, M. (2012). Application of fuzzy logic for predicting roof fall ratein coal mines. Neural Computing & Applications, . http://dx.doi.org/10.1007/s00521-012-0819-3.

Grassi, A., Gamberini, R., Mora, C., & Rimini, B. (2009). A fuzzy multi-attribute modelfor risk evaluation in workplaces. Safety Science, 47, 707e716.

Grima, M. A., Bruines, P. A., & Verhoef, P. N. W. (2000). Modelling tunnel boringmachine performance by neuro-fuzzy methods. Tunnelling and UndergroundSpace Technology, 15, 259e269.

Gürcanli, G. E., & Müngen, U. (2009). An occupational safety risk analysis method atconstruction sites using fuzzy sets. International Journal of Industrial Ergonomics,39, 371e387.

Han, Z. Y., & Weng, W. G. (2010). An integrated quantitative risk analysis method fornatural gas pipeline network. Journal of Loss Prevention in the Process Industries,23, 428e436.

Han, Z. Y., & Weng, W. G. (2011). Comparison study on qualitative and quantitativerisk assessment methods for urban natural gas pipeline network. Journal ofHazardous Materials, 189, 509e518.

Iphar, M., & Goktan, R. M. (2006). An application of fuzzy sets to the DiggabilityIndex Rating Method for surface mine equipment selection. InternationalJournal of Rock Mechanics and Mining Sciences, 43, 253e266.

Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neural-fuzzy and soft computing.Englewood Cliffs, NJ: Prentice-Hall.

Jo, Y. D., & Ahn, B. J. (2005). Amethod of quantitative risk assessment for transmissionpipeline carrying natural gas. Journal of Hazardous Materials, A123, 1e12.

Lecchi, M. (2011). Evaluation of predictive assessment reliability on corroded trans-mission pipelines. Journal of Natural Gas Science and Engineering, 3, 633e641.

Li, Z. (2006). Fuzzy chaotic systems: Modeling, control, and applications. Berlin Hei-delberg: Springer-Verlag.

Liang, W., Hu, J., Zhang, L., Guo, C., & Lin, W. (2012). Assessing and classifying risk ofpipeline third-party interference based on fault tree and SOM. EngineeringApplications of Artificial Intelligence, 25, 594e608.

Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis witha fuzzy logic controller. International Journal of ManeMachine Studies, 7(1), 1e13.

Markowski, A. S., & Mannan, M. S. (2008). Fuzzy risk matrix. Journal of HazardousMaterials, 159, 152e157.

Markowski, A. S., & Mannan, M. S. (2009). Fuzzy logic for piping risk assessment(pfLOPA). Journal of Loss Prevention in the Process Industries, 22, 921e927.

Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Fuzzy logic for processsafety analysis. Journal of Loss Prevention in the Process Industries, 22, 695e702.

Meresht, E. S., Farahani, T. S., & Neshati, J. (2011). Failure analysis of stress corrosioncracking occurred in a gas transmission steel pipeline. Engineering FailureAnalysis, 18, 963e970.

Monjezi, M., & Rezae, M. (2011). Developing a new fuzzy model to predict burdenfrom rock geomechanical properties. Expert Systems with Applications, 38,9266e9273.

Muhlbauer, W. K. (2004). Pipeline risk management manual; ideas, techniques, andresources (3rd ed.). Elsevier Inc.

National Infrastructure Protection Plan (NIPP). (2009). Partnering to enhanceprotection and resiliency. USA: National Infrastructure Protection Plan.

Nieto-Morote, A., & Ruz-Vila, F. (2011). A fuzzy approach to construction project riskassessment. International Journal of Project Management, 29(2), 220e231.

Pandey, M. D. (1998). Probabilistic models for condition assessment of oil and gaspipelines. NDT&E International, 31(5), 349e358.

Rehana, S., & Mujumdar, P. P. (2009). An imprecise fuzzy risk approach for waterquality management of a river system. Journal of Environmental Management,90, 3653e3664.

Ross, T. J. (2010). Fuzzy logic with engineering applications (2nd ed.). John Wiley &Sons, Ltd.

Sadiq, R., & Husain, T. (2005). A fuzzy-based methodology for an aggregativeenvironmental risk assessment: a case study of drilling waste. EnvironmentalModelling & Software, 20, 33e46.

Sadiq, R., Kleiner, Y., & Rajani, B. (2007). Water quality failures in distributionnetworks e risk analysis using fuzzy logic and evidential reasoning. RiskAnalysis, 27, 1381e1394.

Seleznev, V. E., & Aleshin, V. V. (2006). Numerical analysis of fire risk at pipelinesystems of industrial power facilities. International Journal of Pressure Vesselsand Piping, 83, 299e303.

Shahriar, A., Sadiq, R., & Tesfamariam, S. (2012). Risk analysis for oil & gas pipelines:a sustainability assessment approach using fuzzy based bow-tie analysis.Journal of Loss Prevention in the Process Industries, 25, 505e523.

Soszynska, J. (2010). Reliability and risk evaluation of a port oil pipeline trans-portation system in variable operation conditions. International Journal ofPressure Vessels and Piping, 87, 81e87.

Teixeira, A. P., Soares, C. G., Netto, T. A., & Estefen, S. F. (2008). Reliability of pipelineswith corrosion defects. International Journal of Pressure Vessels and Piping, 85,228e237.

Xie, M. (2003). Fundamentals of robotics: Linking perception to action. London: WorldScientific Publishing Co Ltd.

Yazdani, M., Alidoosti, A., & Zavadskas, E. K. (2011). Risk analysis of critical infra-structures using fuzzy COPRAS. Ekonomska Istra�zivanja e Economic Research,24(4), 27e40.

Yazdani-Chamzini, A., & Yakhchali, S. H. (2012). Tunnel Boring Machine (TBM)selection using fuzzy multicriteria decision making methods. Tunnelling andUnderground Space Technology, 30, 194e204.

Yuhua, D., & Datao, Y. (2005). Estimation of failure probability of oil and gastransmission pipelines by fuzzy fault tree analysis. Journal of Loss Prevention inthe Process Industries, 18, 83e88.

Zadeh, L. A. (1965). Fuzzy set. Information and Control, 8, 338e353.

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