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Fuzzy Monte Carlo Simulation and Risk Assessment in Construction
Author(s) -
Sadeghi N.,
Fayek A. R.,
Pedrycz W.
Publication year - 2010
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2009.00632.x
Subject(s) - monte carlo method , probabilistic logic , fuzzy logic , computer science , range (aeronautics) , uncertainty analysis , reliability engineering , probability distribution , construct (python library) , mathematical optimization , data mining , risk analysis (engineering) , operations research , artificial intelligence , simulation , engineering , mathematics , statistics , medicine , programming language , aerospace engineering
Monte Carlo simulation has been used extensively for addressing probabilistic uncertainty in range estimating for construction projects. However, subjective and linguistically expressed information results in added non‐probabilistic uncertainty in construction management. Fuzzy logic has been used successfully for representing such uncertainties in construction projects. In practice, an approach that can handle both random and fuzzy uncertainties in a risk assessment model is necessary. This article discusses the deficiencies of the available methods and proposes a Fuzzy Monte Carlo Simulation (FMCS) framework for risk analysis of construction projects. In this framework, we construct a fuzzy cumulative distribution function as a novel way to represent uncertainty. To verify the feasibility of the FMCS framework and demonstrate its main features, the authors have developed a special purpose simulation template for cost range estimating. This template is employed to estimate the cost of a highway overpass project.