Risk Determination , Prioritization , and Classifying in Construction Project Case Study : Gharb Tehran Commercial-Administrative Complex

Construction projects play an important role in infrastructure projects in developing countries. According to type, size, and complexity of the project, the number and importance of each risk could be different and many projects cannot reach the project goals due to exposure to multiple risks. Many papers have been published on the subject of risk management in construction projects; unfortunately most of them have not been implemented in practical conditions. The aim of this study is to identify and prioritize risks in construction projects.The classical approach used probability and impact for risk assessment, but these criteria do not sufficiently address all aspects of projects risks and there might be a relationship between different criteria.This study proposes the hierarchical dependencies between criteria. A case study of construction project is presented to illustrate performance and usage of the proposed model. Utilizing library studies and interview with experts, managers, and specialists, decision criteria were identified through brain storming. Risks were categorized by the experts into eleven risks. Important risks were evaluated based on the fuzzy ANP, fuzzy DEMATEL, and fuzzy TOPSIS methods. The proposed model is more suitable than the traditional decisionmaking methods in prioritizing risk concerning cost, time, and quality.


Introduction
The risk management can be defined as a process to identify, analyze, and respond to project risks in order to enhance opportunities and reduce threats affecting the objectives of the project.The first step of risk management is risk identification and the next steps can be risk analysis, risk prioritization, and selecting appropriate strategy for dealing with risks.
Construction projects are very risky due to the amount of money invested in them which shows the necessity of identification of risk drivers, the level of each risk effect, intensity of the influence of the risk on the project, and the probability of each risk.Finally, based on these evidences, the appropriate action should be selected by project managers to reduce the loss of projects.
In the case that project managers encounter lots of risk drivers, fuzzy technique is a suitable solution for risk management.Additionally, using fuzzy technique is very helpful to manage uncertain conditions.One of the characteristics of our model in this research is that all aspects of the projects are concerned which is very important in mega projects.Cost, time, and quality are the main elements of project success which should be concerned.
In this paper, by using fuzzy technique, we tried to present a method to identify and prioritize project risks in the Project Life Cycle of construction projects and consequently help managers in decision-making.As a case study, we used a construction project in the west of Tehran and based on the project owner request we do not disclose the company's information.In following sections, firstly, problem statement and importance of research are described.Secondly, a brief review of literature in risk management, risk identification, and fuzzy technique is done.As our research methodology, we used ANP to define appropriate weight for each factor, DEMATEL

Literature Review
Risk is an event or a probable situation, the occurrence of which can have effect on project objectives [1].The occurrence of a risk may have different reasons like economic fluctuation, financial crisis, social changes, and political issues.Also changes in stockholder relationship can be very effective in increasing or decreasing the risk level in projects [2].
Although every construction project has different risks, there are lots of common risks among construction projects.Every project has three main items of cost, time plan, and quality in its objectives and related risks to them are the most common risks.
The construction industry is one of the most important criteria in a country development.Establishment of all infrastructures like roads, power plants, mega projects, and so many other projects needs construction as a part of it [3].
Some researchers believe that construction projects encounter risks more than other projects which are unavoidable but manageable [4].Numerous stakeholders, long time of production, and necessity of interactions between all internal and external parts of a project are the main factors which increase risks in construction projects [5].
The literature review on previous studies of risk management shows that only a few studies at least in the academic level of studies about construction projects risks focused on it.Lack of risk management knowledge and also using systematic approach of identification and solving the risk issues in the field of construction projects are very obvious in previous studies [6,7].
There are different categories of risks.Perry and Hayes categorized the risks into three main drivers, namely, subcontractors, consultants, and customers [2].Cooper and Chapman classified the risks in two categories of primary risks and secondary risks [8].US Department of Energy categorized the risks based on the point of risk effect [1].
Abdou divided construction risks into three categories of financial risks, time risks, and design risks [9].Rezakhani defined eight accounting risks based on the questionnaire he distributed among project experts [10].Carr and Tah used hierarchical organization to categorize risks into two groups of internal and external risks [11].Chapman categorized risks into four groups of environmental, industrial, customers, and project [12].Shen et al. introduced six groups of risk according to the content of the risks including finance, legal, management, market, policy, and politic [13].Chen et al. categorized fifteen main risks of projects into three main categories, namely, resource factors, management factors, and parent factors [14].
Assaf and Al-Hejji defined the project risk elements as effective factors in delay of projects [15].Dickmen et al. used cause-effect diagram to categorize risks of projects [16].Zeng et al. categorized the risk drivers into four groups of human, raw material, instruments, and work yard [17].
Nieto-Morote and Ruz-Vila used fuzzy AHP approach for risk evaluation in their study.Based on a distributed questionnaire, they gathered expert ideas and categorized the project risks into four groups of implementation risks, resources risks, engineering risks, and management risks [18].Rezakhani on his study used Risk Breakdown Structure (RBS) to categorize and rate the risks [10].
Tummala and Burchett used Work Breakdown Structure (WBS) to identify project risks of high voltage transmission lines and categorized all risks into six groups of finance and economy, environmental and political, design, construction, physical, and unexpected events [19].

Research Methodology
3.1.ANP.Just like AHP approach, Saaty also introduced ANP method which was an extended form of AHP while AHP presents a framework with unidirectional hierarchical connections used in multicriteria decision analysis.ANP is a mathematical theory that can systematically overcome all kinds of dependencies [26].

Fuzzy ANP Method.
In the proposed algorithm, fuzzy ANP (FANP) will be used to determine the degree of importance of each of these indicators of prioritization in improvement projects.FANP is a useful method in case which provides a framework for dealing with decision-making problems within which assumptions about dependencies between criteria and alternatives are unnecessary [27].In this method, pairwise comparison matrix for each row of the matrix is filled with the triangular fuzzy numbers.Also, the amount of each parameter is calculated as a triangular fuzzy number.

Fuzzy DEMATEL.
In this research DEMATEL technique is used, too.The Decision-Making Trail and Evaluation Laboratory (DEMATEL) analyzed the criteria causal relationships and used diagrams to show the weights of criteria.This technique was very helpful to solve the problems based on graph technique.Lately, researchers have found that DEMATEL could not be useful to solve uncertainty problems.Therefore, they suggested fuzzy DEMATEL method.The recent method, using fuzzy linguistic variables, solves uncertainty problems [14].

TOPSIS. The Technique for Order of Preference by
Similarity to Ideal Solution (TOPSIS) is a MCDM method which evaluates  alternatives with  criteria.Based on TOPSIS concept and logic, the evaluated alternative should have the shortest distance to positive ideal solution and longest distance to negative ideal solution.According to this logic, the closer solutions to the positive ideal have better ranking.3.5.Data Collection Instrument.Data were collected through questionnaires and interviews.The present study uses below method for data collecting: (1) Expert interviews.
Four questionnaires were used for data collecting contained: identifying risk questionnaire, paired comparisons based on fuzzy ANP, fuzzy DEMATEL questionnaire, and fuzzy TOPSIS questionnaire.The result of the reliability measured with Cronbach's alpha in risk identification questionnaire was  = 0.963.Questionnaires are generally accepted as reliable when Cronbach's alpha is higher than 0.7.In other questionnaires, compatibility is measured.
Research population contains five project teams and each team consists of a project manager, technical expert, deputy executive, project office manager, execution supervisor, and financial manager.For paired comparisons, ten experienced experts specializing in the construction industry were selected.

Case Study
After about three years, Gharb Tehran Commercial-Administrative Complex had only five percent progress.The inability of the company to handle the risks causes delays in the project.Therefore, it is necessary to establish a system for the identification and management of the risks which helps the company to find out efficient solutions against different kinds of risks.
Literature review and using Delphi technique for interviewing and gathering data from fourteen experts within their domain of risk expertise and a minimum of ten years of experience in construction projects led us to identify risks in this research.Experts have recommended sixty-seven risks which are divided into eleven groups.
Literature review helped us to find out 160 risks within past studies (see Table 4).In order to collect the opinion of experts, a questionnaire has been prepared getting the probability of occurrence for each risk.Moreover, the expert has been asked if the questionnaire is comprehensive and if there is a risk that projects are dealing with and was not mentioned in the questionnaire.After collecting comments, a new list of risks has been developed.The results of this questionnaire have been compiled and analyzed and based on the responses another questionnaire has been developed, which was mailed back to the experts.The experts were asked again to leave comments, give suggestions, and answer the questions.The responses to this second questionnaire were compiled and analyzed when a consensus has been reached on risks and a list of 67 risks in eleven groups was prepared as per Figure 1.
ANP method can solve dependency issues and feedback relations among multiple criteria.Thus, this study applies the DEMATEL to establish the correlation between the mutual effects of the criteria and alternatives (group of risks) and uses ANP to determine the weight of each group of the risks.The research processes and architecture are shown in Figure 2.
For finding the interrelations between different criteria, experts were asked to indicate the degree of direct influence of all criteria and alternatives.An average matrix was derived through the mean of the same criteria in the various direct matrices of the experts Table 1.
For normalizing the average matrix, the following formula was used:   After calculating above matrix, total relation matrix was computed by the following formula: Each cell of the matrix is a triangular fuzzy number calculated as follows: In these formulas  is unit matrix;   ,   , and   are  ×  matrix.  ,   , and   are lower limit, medium value, and upper limit of the triangular fuzzy number related to the matrix : R shows the row sum of th row of matrix  and shows the sum of direct and indirect effects of the criteria on the other criteria.D similarly shows the column sum of th column of matrix  and shows the sum of direct and indirect effects that criterion  has received from the other criteria.In addition, when  =  (i.e., the sum of the row and column aggregates), ( R + D ) provides an index of the strength of influences given and received.It means that ( R + D ) shows the degree of Figure 3 illustrates the importance (the horizontal axis) and influence and effect (vertical axis) on each criterion.As seen in the diagram, time is effective and quality and cost are influential.
Similarly, causal relationship between eleven groups of risks in construction projects is determined by using fuzzy DEMATEL technique.
As seen in Figure 4, the criteria of political, legal factors, design, financial, partners, and staffing are at the top of the axis.Therefore, these factors are more influential than other factors.In other words, these are the cause.On the other hand, project management, resources and equipment, planning and implementation, and social environment are at the bottom of the diagram.Among these factors, the most influential was criteria of political and social environment factors that had the greatest influence.
In the next step, the fuzzy ANP method is used to determine the relative weights of a set of the evaluation criteria and obtaining the weight of eleven groups of risks.In order to achieve the purpose of this research, experts were asked for pairwise comparison between criteria that could illuminate the amount of vitality/impact.The relative vitality quality can be resolved utilizing a scale of fuzzy numbers to speak to equivalent significance to great critics.
In this paper, Gogus and Boucher technique have been used to calculate compatibility.In this method, for investigating the compatibility of two matrices (mean number and fuzzy number range), each fuzzy matrix should be deviated into two matrices.First matrix contains medium value   = [  ] and second matrix contains geometric mean of the high and low numbers of triangular fuzzy numbers   = √   ⋅   .
Weight vector matrix is calculated as follows: Largest eigenvalues for any matrix are calculated by using the following formulas: And then the compatibility of each matrix is calculated by the following formulas: , To calculate the incompatibility rate (CR), the following equation is used: The result will be compared with 0.1 threshold.If both of these indexes were less than 0.1, fuzzy matrix is incompatible.The amount of CR should be lower than 0.1.For finding out the indexes' weight, fuzzy ANP statistical method will be used.According to super matrix, four steps have been released for calculating the components' weight.
In the first step, geometrical mean of respondents' pair comparisons about criteria is calculated.Then in the second step eigenvector must be calculated.For calculating eigenvector of all summed pair comparisons tables, logarithm method of minimum square roots will be used as 1/ ,  ∈ {, , } , w = (   ,    ,    )  = 1, 2, 3, . . ., .The third step is formation of eigenvector's matrixes; these matrixes include eigenvectors obtained from pair comparisons of the second step.The fourth step is calculation of final weights of the levels Table 2.In order to calculate final weight of each level's component,   =   ×  (−1) ×  * −1 .

Prioritizing Risk Using Fuzzy TOPSIS Technique
In the previous section, eleven groups of construction risk priorities were set.This section aims to rank risks among eleven groups of risks with conflicting criteria including time, cost, and quality which can help managers and decision makers to find solutions.First, experts' opinions were collected.Because of the verbal expressions, their opinions were ambiguous.In order to use them, it is better to convert these expressions into fuzzy numbers.Decision matrix is created.
In the next step, the fuzzy decision matrix should be normalized.The raw data were normalized utilizing linear scale transformation to bring the different criteria scales into an analogous scale.The normalized fuzzy decision matrix is calculated by the following formula: Then, the weighted normalized matrix should be computed.The weighted normalized value  is calculated by the following formula: In this paper, experts' opinions were given equal weight.
As the fourth step, the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) should be computed by using the following formulas: In the fifth step, the distance of each alternative from FPIS and FNIS should be calculated as follows: In the next step, the closeness coefficient (CC  ) of each alternative should be calculated: CC  represents the distances to the fuzzy positive ideal solution  + and the fuzzy negative ideal solution  − simultaneously.
In the final step, the final list of responses for each important risk would be prepared by ranking the different alternatives according to the closeness to coefficient CC  in decreasing order.The best alternative is the closest to the FPIS and the farthest from the FNIS.
The result has been shown in Table 3.

Conclusion
The main objective of this research was identifying construction project risks and also developing a method to prioritize the risks.As all companies have limited resources to manage and solve all the risks, the prioritization of the risks is unavoidable.Assignment of resources to manage risks should be done based on the priorities and from the top of the list.
As the classic approach to prioritize and manage risks of projects, only probability and effect of each risk are  concerned, but using these two elements is not sufficient and cannot cover all aspects of risk management.Additionally, the above-mentioned items also cannot handle the relation between different risk drivers.Choosing AHP method to prioritize the risks is a way to overcome the limitations of the classic method.
As discussed before, managing our case study in west of Tehran due to high levels of investment on it requires using advanced level of risk management.Cost, time, and quality were three main items on which managers mostly focus to control the projects, but risk management by itself should be noticed as a factor that affects other items.Accordingly, to finalize the project and meet predefined objectives in cost, time, and quality, risk management should be concerned parallel with other objectives.Identification and prioritization of risks in this project can be useful as a managerial toolkit to reduce project failures.
In this project also risks of Project Life Cycle have been identified and shared with project experts in the mentioned company; then all gathered ideas and solutions were shared with project managers to reduce project costs.
These results help managers to focus on the most important risks and problems.Based on the prioritization results by experts ideas, from all the 11 groups of identified risks, financial risks and project management risks were of the most importance.
Reducing financial risk in our project needs integrated and quantitative management of finance with focus on economic risks which can have an effective role in reducing total risk of project.
Project management risk is another important risk among our priorities which mainly happens due to the lack of appropriate experience of the people involved in the project or unsuitable controlling of the human resources.Suggested solutions to reduce this risk are increasing the knowledge level of the human resources, internal and external training, hiring experienced human resources, establishment of project management software, defining rules and responsibilities of the people, and routine reporting from the system.In this study, all risks are considered during life cycle of project.For further study, the ranking of risks from the perspective of the employer, contractor, and consultant will be proposed.Additionally, giving weight to experts and using statistical methods for prioritization of risks are recommended.

Appendix
See Table 4.

Figure 1 :
Figure 1: Analytic network process of risks in construction project.

Figure 2 :
Figure 2: Risk Breakdown Structure for construction projects.

Table 1 :
Average matrix of interrelations of criteria.

Table 2 :
Matrix of final weights of the group of risks.

Table 3 :
Risk prioritization for construction projects.