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 timeconsuming 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 subcontractors 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 subcontractor 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 inputoutput 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
Probability
Rule
Set
Loss
Addition
---------------------------------------------------------------------------------------------------
Category # 6
X
Normalization
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%.
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.
[6] Kostko B. Fuzzy Engineering.
Prentice Hall Publishers. 1997.
[7] Togai Infralogic, Inc. TIL Shell 3.0
BE
[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.
Table 1. The Assessed Critical Risks for a Subcontractor
Risk Category
Acts of God (i = 1)
Fire
Floods
Landslide
Hurricane/Wind Damage
Construction Related (i = 2)
Defective work
Design changes
Different site conditions
Equipment failure
Labor dispute and strike
Labor productivity
Unrealistic schedule
Weather delays
Design Related (i = 3)
Defective design
Defective specifications
Errors and omissions
Inadequate specifications
Incomplete design
Risk Category
Financial (i = 4)
Availability of funds from clients
Cost underestimation
Financial default of any party
Inflation
Tax rate changes
Physical (i = 5)
Damage to equipment
Damage to structure
Labor injuries
Material and equipment theft
Political, Social and Environmental (i = 6)
Changes in laws and regulations
Permits and approvals
Political pressure/disturbances
Pollution and safety rules
Public disorder
Delayed site access/right of way
Disputes/third party delays
Table 2. Fuzzy Rules
Probability
Max. Potential Loss
Risk
VL
VL
VL
VL
L
L
L
L
VL
L
M
H
VL
L
M
H
VL
VL
L
M
VL
L
M
H
Probability
Max. Potential Loss
Risk
M
M
M
M
H
H
H
H
VL
L
M
H
VL
L
M
H
L
M
H
VH
L
M
H
VH
Table 3. Risk Evaluation for a Practical Situation
Considered Parameter for Risk
Calculation
Probability
Max. Potential Loss
(%)
Risk
(%)
Acts of God
Floods
Hurricane/Wind Damage
0.02
0.04
10.5
15.75
8.71
33.1
Construction Related
Design changes
Labor productivity
Unrealistic schedule
Weather delays
0.07
0.08
0.07
0.06
8.75
35
33.25
19.25
38
91.3
91.3
65.8
Design Related
Defective design
0.06
35
91.3
Financial
Availability of funds from clients
Cost underestimation
0.045
0.05
29.75
24.5
70.1
66.1
Physical
Labor injuries
0.08
21
69.3
Political, Social and
Environmental
Permits and approvals
Delayed site access/right of way
Disputes/third party delays
0.04
0.05
0.04
21
19.25
19.25
44.9
52.3
41