Issue 20

P. Rezakhani, Frattura ed Integrità Strutturale, 20 (2012) 17-21; DOI: 10.3221/IGF-ESIS.20.02 19 Kangari and Riggs [35] presents a risk analysis model, which makes use of Fuzzy set theory (FST) as a risk assessment tools, consists of three modess follows; natural language computation, fuzzy set evaluation of risk, and linguistic approximation [44]. Specifically, the linguistic approximation method handle subjectivity issues in construction risk assessment by finding the nearest natural language expression for the estimated fuzziest using Euclidean distance in order to. Peak et al. [36] propose a risk pricing model that en determines the bid price of a construction project. The model estimates the risk-associated consequence as fuzzy numbers that represent risk consequences to reflect the uncertainty involved in determining the bid price. The fuzzy numbers are assumed by two intervals; i.e., the most likely and the largest likely intervals which are obtained from either historical data or expert opinions. The model applies fuzzy arithmetic operation to compute the risk contingency value and . a ranking method to calculate the value of risk contingency in terms of monetary cost by transferring the fuzzy number into crisp value. The method was verified by applying it to a real construction project. Tah and McCaffer [20] introduced a computer tool which approximates the amount of contingency cost in PASCAL programming. The system determines the risk level in linguistic terms to be used as the basis of the contingency allocation for tender preparation. . It proposes a new risk breakdown structure called a hierarchical risk- breakdown structure (HRBS) which presents contractor’s risk. Wirba et al. [21] presented a fuzzy set theory-based risk assessment approach which identifies risks, checks for dependencies amongst them, and assesses risk likelihood of occurrence by using linguistic variables. Carr and Tah [37] presented a fuzzy set based qualitative risk assessment model which implements hierarchical risk breakdown structure. The model allows to define the risk descriptions and their consequences using linguistic variables and to formulate the rules using the relationship between the likelihood of occurrence (L), the severity (V), and the effect of risk factor (E), i.e., “If L and V then E”. Fuzzy approximation and composition were performed to identify and quantify the relationship between risk sources and the consequences on project performance measures. It evaluates the risk exposure by assessing the consequences relative to project performance measures (i.e., time, cost, quality and safety, etc) using the fuzzy estimates of the risk components. Cho, et al. [38] proposed an uncertainty range estimate method which incorporates uncertainties using fuzzy concepts. The method introduces some forms of fuzzy membership functions that that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments. The method uses linguistic variables (i.e., “close to any value” or “Higher/Lower than analyzed value”, etc) which include some quantification with giving specific value by defining three membership functions(i.e., “Close to”, “Lower than”, and “Higher than” curves). Choi, et al. [39] presented a fuzzy- based uncertainty assessment system which considers uncertainty as objective probabilities and subjective judgment by incorporating probabilistic or linguistic variables. The system was under rigorous tests with underground construction. It implements four steps, i.e., identifying, analyzing, evaluating and managing the risks inherent in construction projects. It was confirmed that the system accommodate both probabilistic data obtained from historical data and subjective data obtained from expert group. An et al. [44] proposed risk assessment or risk management system for construction project. This system has also proposed a risk analysis method as a part of the risk management system developed. Dikmen et al. [42] propose a fuzzy risk rating method which rates the risk involved in cost overrun in international construction projects. The model introduces “Controllability” or “Manageability” concepts into the contractor’s decision making which determines if the contractor enters into international market. It allows assessing the contractor’s decision using four categories, i.e., internationalization, market selection, project selection, and markup selection. The system identifies risks, models the risks using influence diagrams, selects membership function of each variable, captures the experts’ opinion using aggregation rules, aggregates fuzzy rules into a fuzzy cost overrun risk rating, carries out fuzzy operations, and determine the risk level of an international project by quantifying the final risk rating. Zeng et al. [30] hybridized fuzzy reasoning and AHP approach to to handle subjective assessments and to prioritize diverse risk factors, respectively [44]. The model quantifies the risk magnitude (RM) of risks a by integrating a risk parameter called factor index (FI) which evaluate the magnitude of possible risk and combine it with risk likelihood (RL) and risk severity (RS) into the fuzzy inference system. The system utilized a modified fuzzy AHP to capture and convert expert’s fuzzy information and subjective judgment. Wang and Elang [46] proposed a fuzzy multi criteria and multi participant decision making approach which allows decision makers to rapidly and effectively evaluate multiple fuzzy risk factors using linguistic terms by aggregating the assessments of multiple risk factors. Zhang and Zou [45] proposed a methodology which produces the appraisal vector of risky conditions of the construction project by aggregating the weight coefficient of risk groups, and fuzzy risk factors obtained from experts using the AP technique, a hierarchical structure of risks, and the fuzzy evaluation matrixes of risk factors. Nieto et al. [15] proposed an algorithm to handle the inconsistency in the fuzzy preference relation when pair-wise comparison judgments are necessary. Karimiazari et al. [3] proposed an extended version of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) which solve the multi criteria risk assessment model under a fuzzy environment. Lyons and Skitmore [40] describes the common procedure that all fuzzy risk assessment methods retain as follows; The first step, Definition and measurement of parameters, is to define the risk