A review of fuzzy risk assessment models for construction projects

Rezakhani, Pejman

doi:10.2478/v10189-012-0016-5

Cited by 16 publications

(14 citation statements)

References 26 publications

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“…The most critical risk factors can be distinguished as capturing more attention (Rezakhani, 2012). Many risk factors that affect time and cost objectives are identified for many case studies (Rezakhani, 2011;Zhang & Zou, 2007;Singh & Trivedi, 2012;Senouci, Ismail, & Eldin, 2016;Abd El Khalek, Aziz, & Kamel, 2017;Nawar, Hosny, & Nassar, 2017;Issa & Ahmed, 2014;Issa, Farag, Abdelhafez, & Ahmed, 2015). The risk assessment process requires an assessment of the probability or likelihood of the risk and impact (Zhang & Zou, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Evaluating and analyzing the risks of a project and planning their management are the most critical steps in the project definition stage (Gohar et al, 2012). Risk management aims to identify the sources of risk and uncertainty, determine their impact, and develop appropriate management response (Rezakhani, 2011). The majority of risk analysis tools are based on statistical decision theory.…”

Section: Introductionmentioning

confidence: 99%

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A Model for Evaluating the Risk Effects on Construction Project Activities

Issa

Mosaad

Hassan

2019

Journal of Civil Engineering and Management

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Cost overruns and time delays are considered to be very important challenges for the majority of construction projects. These challenges are typically attributed to their associated risks. Due to the risky and uncertain nature of construction projects, an increasing amount of attention is given to estimating and overcoming cost overruns and time delays. New techniques are being developed to help project managers to contractually complete projects within cost and time constraints. The objective of this study was to develop a new qualitative and quantitative risk analysis model that can be employed for construction projects. The proposed model, which is based on a fuzzy logic tool, consists of two modules for assessing risk factors that affect the main construction activities and computing the expected cost overruns and time delays that are associated with these risks. Using numerous logical rules, the model applies the probability of occurrences and impacts of the risks on the cost and time of the main activities. The Spearman and Kendall correlation coefficient tests are applied to verify and select a suitable membership function. Using four proposed membership functions, the results of these tests confirmed that the triangle membership function is suitable for the model. The model is verified by application to HVAC system activities in two actual construction projects, which serve as case studies. Two different methods are proposed and applied to quantify the cost overruns and time delays. The first method is based on determining the cost overruns and time delay values for each activity according to their weight in the system. Triple premise rules are proposed and applied in the second method, which is established to relate all activities. The results from the second method are more accurate compared with the first method based on actual data from the case study projects. In addition, the results demonstrated that the proposed model can be used to quantify the expected cost overrun and time delays in construction project activities and can be generalized and implemented in different construction activities.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Model for Evaluating the Risk Effects on Construction Project Activities

Issa

Mosaad

Hassan

2019

Journal of Civil Engineering and Management

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“…The aim of risk management is to identify the sources of risk and uncertainties, to determine their in uence, and to develop an appropriate management tool to respond to risk items [4]. For the risk management processes, a range of methods have been suggested by di erent researchers.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Safety Risks in Construction Using Fuzzy Failure Mode and Effect Analysis (FFMEA)

2016

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The construction industry is one of the most dangerous environments to work in. For this reason, safety-related risk analysis is one of the most signi cant tasks that has to be undertaken when managing major construction projects. A combination of fuzzy logic and Failure Mode and E ects Analysis (FMEA), Fault Tree Analysis (FTA), and Analytical Hierarchy Process-Data Envelopment Analysis (AHP-DEA) was applied to improve the process of managing safety risks. Two di erent types of large-scale construction projects were also considered as case studies. It was found that the risk of falling from a height is the most signi cant risk in both types of project. Moreover, the factors intensifying the risk of injury in the workers who fall were found to be ignoring safety and lack of personal protective equipment as well as lack of appropriate training for construction workers. It was also concluded that the framework is applicable to all construction sites, covers all safety aspects, and has valid results.

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“…It is well accepted that Fuzzy Set Theory (FST) provides a useful way to deal with ill-defined and complex problems in decision making by quantifying imprecise information, incorporating vagueness, and making decisions based on imprecise and vague data [10].The method allows for the translation of a subjective judgment given in linguistic expressions (i.e., "low," "high," etc.) into mathematical measures.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…After Zadeh introduced the concept of fuzzy sets and theory, researchers such as Kangari and Riggs, Peak, Tah and McCaffer, Wirba, Carr and Tah, Cho, Choi , Lyons and Skitmore, Baker and Zeng , Dikmen, Zeng, Wang and Elang, Karimiazar and Nieto used fuzzy set theory (FST)-based risk modeling and analytic methods that deal with illdefined, vague, imprecise, and complex risk analysis problems [10]. Ever since Zadeh's contributed in 1965 to this new field of fuzzy logic, there has been much literature in this field.…”

Section: Fuzzy Logicmentioning

confidence: 99%