Fuzzy Logic: An Appropriate Technique for Effective Risk Analysis and Decision Making for Construction Projects

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 12, December 2015)

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Fuzzy Logic: An Appropriate Technique for Effective Risk

Analysis and Decision Making for Construction Projects

Savita Sharma

1

, Pradeep K. Goyal

2 1

ResearchScholar, Gyan Vihar University, Jaipur, India

2_{Associate Professor, Dept. of Civil Engineering, Govt. Engineering College, Ajmer, Rajasthan, India} Abstract --- Risk management is an important and crucial

matter for project manager as the success of any project very much depends on the associated risks and uncertainties in the project. A systematic risk management process can be divided into risk classification, risk identification, risk analysis and risk response. Risk analysis is the second stage in the Project risk management (PRM) where risks which have the highest impact on the project, are short listed out of all the identified risks. There are number of techniques available for risk assessment in the literature, which have their own advantages, disadvantages and limitations. The purpose of this paper is to review and discuss risk analysis techniques which can be used for risk analysis for construction industry. The strengths and weaknesses of the techniques are highlighted and discussed. In this paper, we are illustrating the use of fuzzy logic theory for risk analysis and decision support system in construction industry, as according to author this theory has the potential to handle the complex and dynamic nature of construction industry.

Keywords---Construction management, Simulation, Risk, Fuzzy logic, Probability

I. INTRODUCTION

Risk management is considered to be an important and crucial matter for project manager as the success of any project very much depends on the associated risks and uncertainties in the project. It is observed that construction projects are plagued by risks and uncertainties due to the various factors such as contribution of numerous participants, long construction durations, interaction between internal and external environments and the complex & dynamic behaviour of the construction activities. These risks are sure to be increased with the rapid advancement of the construction especially in developing countries like India. Therefore it has become the need of time for project managers to develop a systematically and integrated approach to reduce the risk in construction projects. Thus, project risk

management (PRM) is one of the important areas of interest for both researchers and construction managers [1].

Risk management is a systematic way of finding areas of risk and how each should be treated. It is a tool for identifying sources of risk and uncertainty, determining their impact, and developing appropriate management responses [2].

A systematic risk management process can be divided into risk classification, risk identification, risk analysis and risk response. Risk responses are further categorized into four actions, i.e. retention, reduction, transfer and avoidance [3, 4].

Risk analysis is the second stage in the PRM where risks which have the highest impact on the project, are short listed out of all the identified risks [5]. The objective of risk analysis is a precise and objective calculation of risk and making decisions.

The purpose of this paper is to review and discuss risk analysis techniques which can be used for risk analysis for construction industry. The strengths and weaknesses of the techniques are highlighted and discussed. In this paper, we are illustrating the use of fuzzy logic theory for risk analysis and decision support system in construction industry, as according to author this theory has the potential to handle the complex and dynamic nature of construction industry.

II. RISK ANALYSIS TECHNIQUES AND THEIR CLASSIFICATION

The appropriate and efficient techniques are required for the risk analysis of any project. There are number of techniques available in the literature, which have their

own advantages, disadvantages and limitations.

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Fig.1: Classification of risk analysis techniques

A. Qualitative analysis

In qualitative analysis, risks are assessed on the basis of descriptive scales. Each risk and its impacts are described in terms of qualitative scale such as high/medium/low. For identifying requirements for further analysis or action Risk events are ranked and assessed after combining their probability of occurrence and impact [7]. These methods are used where quick assessment are required [5]. Risk probability and impact assessment, Probability/impact risk rating matrix, Risk categorization and risk urgency assessment are the techniques which fall under this type of analysis. As indicated by Lyons and Skitmore [8], the qualitative approach is the most common type of technique to analyze risks. Barnes [9] modelled risk as probability and impact (P-I) with risk impact defined as a variance in cost estimate. P-I model has been widely used. In spite of that this model has limitations as expressed by many researchers [10]. The strengths and weaknesses of the qualitative analysis are given below:

Strengths

 The method is easy to use.

 Not much mathematical skills are required for assessing the risk.

 Methods can be understood easily and appeared to

be more accessible.

 These methods are very helpful when sufficient data are not available for quantitative assessment

Weaknesses

 Do not quantify the risk exactly.

 Methods are depended on the personal judgment and past experiences therefore results are varied from person to person.

B. Quantitative analysis

In this approach, the effect of identified risk on the project are analysed numerically. Quantitative risk analysis quantifies the combined effect of risk on project objectives. These methods are based on probability distribution of risks. For quantitative risk analysis usually Probability Theory (PT) based tools are used [11]. This method of analysis is useful and gives more accurate results, if sufficient and adequate data are available. Birnie and Yates [12] explored the use stochastic modelling into construction cost estimating and forecasting. It has been suggested that use of utility theory, decision tree and the Monte Carlo simulation techniques can be used in the prediction of cost. The study was conducted on a housing refurbishment contract. In this study, the actual project final cost was compared with the predicted range. The result showed that figure was within the predicted range. The strengths and weaknesses of quantitative analysis are given below:

Strengths

 These methods may provide more objective results,

if ample data are available.

Risk Asseessment

Techniques

Qualitative

Risk probability and impact assessment,

Probability/impact risk rating matrix,

Risk Categorization

Risk Urgency Assessment

Quantitative

Monte Carlo simulation,

Scenario analysis

Sensitive analysis

Event tree analysis

Fault tree

Failure mode and effect analysis

Analytical

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 These methods utilize more sophisticated

techniques and tools to analyze construction project risks.

Weaknesses

 Due to the uniqueness and non repetitive

character of construction projects sufficient and adequate data are not available for obtaining objective probabilities to quantify the risk.  It is found very difficult to exactly quantify the

effects and consequences of risk because of association of many factors with a high level of uncertainty.

 There are involvement of large number of

assumptions, judgments and opinions

 Uniqueness of construction projects decrease the

significance and reliability of statistical

aggregates which are derived from probability-based analysis.

C. Analytic hierarchy process

Analytic Hierarchy process (AHP) is a common method of multicriteria decision making. This method is used for organizing and analyzing complex decisions and is based on mathematical and psychological sciences. It incorporates the experience, the knowledge and the intuition of the decision makers [13]. In this method, decision elements and their corresponding alternatives are classified after making comparisons between possible pairs of each group. This method used by Mustafa and Al-Bahar [14], Azuma and Miyagi [15] for construction projects. Bhushan and Raj [16] found that this method is very useful for decision making with high risks and uncertainties with human perceptions and judgments. The strengths and weaknesses of the Analytic Hierarchy process (AHP) are given below:

Strength

 Method is easy to understand.

 Method is based on systematic judgement.

 Useful for decision making with high risks and uncertainties

Weakness

 Due to the involvement of a large number of judgments, inconsistency problem may occur.

 AHP is not found suitable in rank reversal problem

in certain situations. Rank reversal may take place if a new alternative is introduced (which does not change the range of outcomes of any criterion) due to this the previous assessment is to be rejected.  The conversion from semantic to numeric scale

which is used to measure the strength of preference, is sometimes not seemed to be suitable.

D. Dempster-Shafer theory

It is also called theory of belief functions as it is a generalization of the Bayesian theory of subjective probability [17-18]. The Dempster-Shafer theory is used for expressing and interpreting with uncertain, imprecise and incomplete information [19]. Obtaining degrees of belief for one question from subjective probabilities for a related question, and Dempster's rule evidence for combining such degrees of belief of independent items of evidence are the basic ideas on which this theory works [17-18]. The strengths and weaknesses of the Dempster-Shafer theory are given below:

Strength

 It presents the relationships of variables and is easy to understand.

 It estimates the conditional probability and

distribution. Specific conditions are taken into account, and a range of values is provided for better informed decision-making.

Weakness

 It is not suitable for complex issues involving many variables. It may be too expensive to determine the relationships and conditional probability functions.

 It may be difficult to determine conditional

probability without experience data.

 Its main shortcoming is, however, the elicitation and interpretation of belief functions. Furthermore, the computational methods employed in the theory are very complex and thus, of little practical use. This is the reason why it has had very little application

 Theory is richer in terms of semantics since it allows an expression of partial knowledge.

E. Fuzzy theory

Fuzzy set theory is a branch of modern mathematics was introduced by Lotfi A. Zadeh [20] to model vagueness intrinsic to human cognitive process. It is suitable to handle the ill defined and complex problems due to the partial and imprecise information for decision making. Fuzzy sets are able to incorporate information described in linguistic terms. Many researchers [21-24] used the process in construction industry for risk analysis.

Strength

 It is an appropriate mathematical modelling

framework for risk analysis.

 It is a useful way to tackle the uncertain or approximate reasoning that characterize the real-world systems.

 Fuzzy set theory has fewer limitations as compared

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 Fuzzy set theory is helpful in making rational

decisions in uncertain and conflicting situations.

 Membership functions are useful for vague input in

the modelling

 The process of modelling can be

easily understood and verified intuitively

 By using membership functions, the results of the scaling membership functions can be scaled.

Weakness

 The process suffers from the problem of execution of arithmetic procedure.

 Limitation of aggregating risk assessments.

Average assessment solution can be obtained by existing methods.

III. APPROPRIATENESS OF FUZZY LOGIC IN CONSTRUCTION PROJECTS

Based on the extensive literature review, it is observed that qualitative analysis is commonly adopted for the ease of the method. Traditional risk models in construction industry are based on probability and classical set theory. But as the construction projects have the nature of non repetitiveness, the data for quantitative analysis are not seemed to be adequate. AHP and Demster Shafer theories also have some limitations. Each theory has its own advantages, disadvantages and no theory is found perfect for handling the uncertainty.

After discussions of strengths and weaknesses of the risk analysis process, in the opinion of the author fuzzy theories are found more suitable for tackle the complex problems in construction industry as the process is based upon experience, assumptions and human judgment. The theory can be applied in various phases of risk analysis in construction. The risks can be ranked according to the level of severity so that required action can be taken to manage the risks without any delay. The theory is used to quantify the probability of the project delay and cost overrun risk and therefore plays an important role for decision making and strategic planning in construction projects.

IV. APPLICATION OF FUZZY LOGIC THEORY FOR RISK ANALYSIS IN CONSTRUCTION

In this section the applicability of fuzzy theory in construction projects is being demonstrated with the help of a numerical example. A model is developed for determining the probability of risk and decision making. The following procedure is adopted to calculate the rank and assess the importance index of risk factors. The risk factors prevailing in the construction industry are identified through a questionnaire survey. For judging the level of importance of identified factors. The relative importance index (RII) is calculated by using the relation given [25] below:

*

W

RII

A N





Where W is the weighting given to each factor by the respondent (ranging from1 to 5), A is the highest weight and N is the total number of respondent.

A.Modelling in fuzzy inference system

In this step, the fuzzy logic model is designed for predicting the probability of risk. The fuzzy logic process can be defined as rule-based systems, in which the input is first fuzzified (i.e., converted from a crisp number to a fuzzy set) and subsequently processed by an inference engine. This engine retrieves the knowledge in the form of fuzzy rules contained in a rule-base. The fuzzy sets computed by the fuzzy inference as the output of each rule are then composed and defuzzified (i.e., converted from a fuzzy set to a crisp number). Fuzzy Logic allows the mapping of the linguistic values in a way that mimics precise numerical analysis by using membership structure that organizes the data. the main steps for the process are as follows:

Step 1. Independent variables are selected as the key determinants or indicators of the dependent variable. Step 2. Fuzzy sets are created for both independent and dependent variables. Instead of using the numerical value, fuzzy sets in terms of human language are used to describe a variable. The degree of truth that each variable belongs to a certain fuzzy set is specified by the membership function.

Step 3. Inference rules are built in the system. A fuzzy hedge may be used to tweak the membership function according to the description of the inference rules.

Step 4. The output fuzzy set of the dependent variable is generated based on the independent variables and the inference rules. After defuzzification, a numerical value may be used to represent the output fuzzy set.

Step 5. The result is then used for informed decision-making. Fig. 2 shows the basic steps of fuzzy logic process

B. Numerical Example

The following risks are considered for the study  economic and financial,

 contractual and legal,

 physical and construction related,

 managerial and performance related,

 Political and societal.

(i)Ranking of risk factors

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C. Analysis steps for the model development

 To develop the model, following steps are

performed on fuzzy logic tool box of MATLAB.



Construct a five input, one output system in the FIS editor. The identified risk factors and “risk probability” are entered as input members and output member respectively. These are shown in Fig. 3.

[image:5.595.326.537.128.432.2]

Fig. 2: Basic steps of fuzzy logic process

(ii) Membership functions associated with all of the input and output variables are defined in membership function editor as shown in Fig.4. All the parameter related to their membership function of each variable is given in the Table1.

(iii) In order to perform fuzzy inference, rules which connect input variables to output variables are defined. For the present model 25 rules are constructed in the form of IF-THEN. Five of them are given below.

Rule1: if the probability of economic and financial risk is very low the risk probability is very low

Rule2: if the probability of economic and financial risk is low the risk probability is low

Rule3: if the probability of economic and financial risk is medium the risk probability is medium.

Rule4: if the probability of economic and financial risk is high the risk probability is high.

Rule5: if the probability of economic and financial risk is very high the risk probability is very high.

(iv) The relative importance indices (RII’s) of risk factors are assigned as weightage to the fuzzy rules to develop the assessment model to estimate the probability of risk. Since the RII’s of the risk factors have different values, the fuzzy rules weights will differ accordingly. So that each if-then rule will have different weights, showing relative importance of fuzzy rules. These are presented in Table 2.

Fig. 3: Input and output members for risk analysis model

[image:5.595.310.551.138.714.2]

(v) The rule viewer displays a roadmap of the whole fuzzy inference process. The rule viewer shows how the shape of the certain membership function influences the overall result. Fig. 5 shows the rule view of the system.

Fig. 4: Membership function for the risk assessment model independent variable (input)

fuzzification

rule evaluation

defuzzification

[image:5.595.313.562.498.760.2]

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[image:6.595.323.539.125.445.2]

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Table 1:

linguistic variables used in model and their membership function

variables Range MFs No of

MFs

Name of the parameters Input parameter

[image:6.595.42.288.155.456.2]

economic and financial,

[0 -1] trapmf 5 1.very low 2.low 3.medium 4.high 5.very high contractual

[image:6.595.314.553.443.757.2]

and legal

[0 -1] trapmf 5 1.very low 2.low 3.medium 4.high 5.very high physical and

construction related,

[0 -1] trapmf 5 1.very low 2.low 3.medium 4.high 5.very high managerial

and performance related,

[0 -1] trapmf 5 1.very low 2.low 3.medium 4.high 5.very high Political and

societal.

[0 -1] trapmf 5

1.very low 2.low 3.medium 4.high 5.very high (vi) Finally, the input-output mappings are obtained by

choosing view menu and under it view surface.

Table 2:

Sample fuzzy rules for the of risk assessment model and rules weight

S N

Rules

Rule weight

1 if the probability of economic and financial risk is very low the risk probability is very low

.82

2 if the probability of contractual and legal risk is very low the risk probability is very low low

.8

3 if the probability of physical and construction risk is very low the risk probability is very low

.78

4 If the probability of managerial and performance related, is very low the risk probability is very low

.72

5 if the probability of Political and societal. is very low the risk probability is very low

.9

[image:6.595.43.285.483.714.2]

Fig. 5: Defuzzification process for the risk analysis model

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V. CONCLUSION

The paper has reviewed the existing literature on the risk assessment theories. The theories have their own advantages and disadvantages. No theory seems to be perfect. Practical experience, personal judgment and intuition play an important role in decision making. We propose FST as an appropriate theory for tackling the ill-defined and complex problem of construction industry as the theory has the potential to model vagueness intrinsic to human cognitive process. The theory can be applied in various phases of risk analysis in construction. The risks can be ranked according to the level of severity so that required action can be taken to manage the risks without any delay. The theory is used to quantify the probability of the project delay and cost overrun risk and therefore plays an important role for decision making and strategic planning in construction projects.

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