Risk Evaluation Using a Fuzzy Logic Model

Risk Evaluation Using a Fuzzy Logic ModelAmaury Caballero, Syed Ahmed, and Salman Azhar Department of Construction Management Florida International University 10555 W.. This research use

Risk Evaluation Using a Fuzzy Logic Model

Amaury Caballero, Syed Ahmed, and Salman Azhar

Department of Construction Management Florida International University

10555 W Flagler Street, Miami, Florida 33174

USA

Abstract:- Construction is a highly risk-prone industry with not a very good track in dealing

with risks The participants of the industry, as a result, have been enduring the agonizing outcomes of failure in the form of unusual delays in project completion, with cost surpassing the budgeted cost and sometimes even failing to meet quality standards and operational requirements Thus, effective analysis and management of construction-associated risks remain a big challenge

to the industry practitioners This research uses as a basis a questionnaire survey and in-depth interviews conducted in the State of Florida, and starting from this, propose a risk management fuzzy logic model for the construction sub-contractors The proposed model is based on a systematic methodology of risk identification, classification, analysis and response The model is expected to help subcontractors to get an initial quantified idea, based on the responses of experts,

of the incurring risks in a project development.

Key Words: - Construction, Risk management, Project management, Fuzzy logic models.

1 Introduction

Different parties in a construction

project face a variety of uncertain

factors These factors can be complied

under the category of risk Making

decisions on the basis of assumptions,

expectations, estimates and forecasts of

future events involves taking risks Risk

and uncertainty characterize situations

where the actual outcome for a particular

event or activity is likely to deviate from

the estimate or forecast value [1]

Construction risk is generally perceived

as events that influence project

objectives of cost, time and quality [2]

The construction industry has long been

recognized as particularly risk laden and

subject to more risk and uncertainty than

many other industries Some of the risks

associated with the construction process

are fairly predictable or readily

identifiable; others may be totally

unseen [3] The process of taking a

project from initial investment appraisal

to completion and into use is complex, generally bespoke, and entails time-consuming design and production processes It requires a multitude of people with different skills and interests and the co-ordination of a wide range of disparate, yet interrelated, activities Such complexity moreover, is compounded by many external, uncontrollable factors [4]

In the context of project management,

risk management is defined as: " A formal orderly process for systematically identifying, analyzing, and responding to risk events throughout the life of a project to obtain the optimum or acceptable degree of risk elimination or control" [5] In practice, a risk management system must be practical, realistic and must be efficient on cost

and schedule control In construction

industry, an effective risk management

system depends very much on the

characteristics and conditions of the

project and the attitude of the individuals

of the decision- making group

2 Identification of Critical Risks

Risk identification process is the first

step in risk management modeling It is

the process of systematically and

continuously identifying, classifying,

and assessing the risks associated with a

construction project In this research, the

critical risks were identified in three

stages as follows:

 Identification of all possible

risks, which may be encountered

by a subcontractor through

detailed literature and Internet

search

 Identification of critical risks in

the Florida construction industry

These risks were identified from

the list generated through a

questionnaire

 Verification of critical risks in

the Florida construction industry

via interviews with professionals

Both quantitative and qualitative

analysis was performed depending on

the nature of data collected through

questionnaire and interviews The

analysis includes the identification of

key critical risks as shown in Table 1

3 Development of the Risk

Management Fuzzy Logic Model

The risk management model for the

sub-contractors was developed based on

a systematic methodology of risk

identification, risk classification, risk

allocation and risk response This risk

management information, obtained

from Table 1 can be used by

sub-contractors to accurately classify the

identified risk element; estimate their

probability of occurrence to decide

whether to avoid the risk completely,

retain it and try to reduce its impact by

taking preventive steps; or finally,

transfer it to a party better able to

handle it

The mathematical model gives the sub-contractor a quantified evaluation of the risk that can be used as an element to compare different projects

Lets define the vector P, as the

probability of occurrence of the different possible events For the risk

category i, where i can take values from

1 through 6, this vector can be represented through:

P = [Pi1, Pi2, ………., Pin ], (1) Where Pin is the risk number n in the risk category i The value for any element can vary from 0 to1, and n will

vary in general from one category to another

The vector M will represent the

Maximum Potential Loss, expressed as

a percent of the total cost lost due to each event Where

M = [Mi1, Mi2,……….,Min], (2) Presented in a similar way to the

previously defined P.

The presented situation can be solved using Fuzzy logic The use of fuzzy rules provides a systematic way of solving imprecise, ambiguous, and vague input-output relations [6] There exist several advantages when implementing decision-making models based on fuzzy logic:

1) Experts related to the problem area can present their evaluation

of the different parameters with concepts as “worse”, “better”, etc, without having to numerically quantify their opinions from the beginning of the evaluation process

2) The calculus using fuzzy logic is simple and close to the representation of knowledge 3) There is a wide array of software available for solving problems utilizing fuzzy logic

The two main factors affecting the risk

are the Probability of Occurrence of any

event and the Maximum Potential Loss

They are presented as fuzzy variables as

well as the Risk, which is the output

The output is represented, as numbers

varying from 0 to 100, where 0 is no

risk at all and 100 is the certainty of

occurrence of a non-desired situation

The selected membership functions for

each input fuzzy variable are: VL

Very Low, L Low, M Medium, and

H High For the output fuzzy variable,

it is added VH Very High

For applying fuzzy logic to each category, the presented rule set on Table

2 was employed The rules structure is

of the type “if X and Y, then Z” This rule set may be changed in dependence

of the real conditions under which the project is developed After the defuzzification a number was obtained for the risk related to each category, and finally added to the other numbers representing each of the six considered categories affecting the final result As the final number will be in general more than 100, it becomes necessary to rationalize All this is represented in the block diagram of Figure 1

Category # 1

- X

Risk (0% to 100%) Figure 1 Block diagram Representing the Necessary Operations for the Risk Calculation

Example:

Table 3 represents a practical situation

in South Florida The numbers for the

probabilities of occurrence of the

different events have been obtained

from experts The universe of discourse

for each fuzzy variable was taken as

follows:

Probability: 0.001 to 0.1

Maximum Potential Loss: 0% to 35%

Risk: 0% to 100%

The numbers for the maximum potential

looses have been obtained from

surveys In this example, only the

factors with high incidence in this particular place have been taken into account The used fuzzy logic software [7] gives the results Figure 2 shows the surface representation for the Risk as per the selected ranges of the input variables and the established fuzzy rules

Under the specified conditions, assuming statistical independence among all the events and giving them the same weight, the obtained risk average is 58.7%

Probability

Loss

Rule Set

Category # 6

Addition

Normalization

Figure 2 Risk Surface Representation X – Probability, Y – Loss, Z - Risk

4 Conclusions

The concept of risk management is

relatively new to the Florida

construction industry The responses to

the questionnaire reveal that formal risk

management is not being carry out by

most subcontractors In fact, some

responses were received stating that

they were not aware of a discipline

called risk management It appears that

Florida subcontractors are still not

aware of the great benefits that risk

management provides to the

construction industry It is found that

the Florida construction industry prefers

to eliminate and transfer risks instead of

finding as systematic procedure to deal

with them through such as risk retention

or risk reduction

The developed fuzzy model can help in:

 Identification of all possible

risks, which may be encountered

by a subcontractor

 Identification of critical risks in

a construction project

 Giving an idea of the risk involved in a project

References:

[1] Raftery, J Risk Analysis in Project

Management, E & FN Spon, London

SE1 8HN, UK 1994

[2] Akintoye, A.S., and Macleod, M.J Risk Analysis and Management in the

Construction, International Journal of

Project Management, Vol 15, No 1, pp.

31-38 1997

[3] Smith, R.J., and Gavin, W Risk Identification and Allocation: Saving Money by Improving Contracts and Contracting Practices A special report

presented to the ASCE Hong Kong

International Group and the Chartered Institute of Arbitrators (HK), March

1998

[4] Flanagan, R., and Norman G Risk

Management and Construction

Blackwell Scientific Publications,

Oxford, London 1993

[5] Al-Bahar, J.F Risk Management in

Construction Projects: A Systematic

Analytical Approach for Contractors,

Ph.D Dissertation, Department of Civil Engineering, The University of California at Berkeley, 1988

[6] Kostko B Fuzzy Engineering.

Prentice Hall Publishers 1997

[7] Togai Infralogic, Inc TIL Shell 3.0

BE

Table 1 The Assessed Critical Risks for a Subcontractor

Acts of God (i = 1) Financial (i = 4)

Tax rate changes

Construction Related (i = 2)

Labor productivity

Permits and approvals

Design Related (i = 3) Political pressure/disturbances

Incomplete design

Table 2 Fuzzy Rules

Probability Max Potential Loss Risk

Probability Max Potential Loss Risk

Table 3 Risk Evaluation for a Practical Situation

Considered Parameter for Risk

Calculation Probability Max Potential Loss

Acts of God

Construction Related

Design Related

Financial

Physical

Political, Social and

Environmental