Risk Evaluation Using a Fuzzy Logic Model

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 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 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 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 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 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 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 to 100, where 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 Probability For applying fuzzy logic to each category, the presented rule set on Table 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 Category # Loss Rule Set Addition - Category # X Normalization Risk (0% to 100%) Figure Block diagram Representing the Necessary Operations for the Risk Calculation Example: Table 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 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 Risk Surface Representation X – Probability, Y – Loss, Z - Risk 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 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 Table Fuzzy Rules 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 Probability Max Potential Loss Risk VL VL VL VL L L L L Probability VL L M H VL L M H Max Potential Loss VL VL L M VL L M H 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 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 ... Rules 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... L M H VH Table 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... methodology of risk identification, risk classification, risk allocation and risk response This risk management information, obtained from Table can be used by subcontractors to accurately classify the