ITcon Vol. 25, pg. 87-98, http://www.itcon.org/2020/5

Risk quantification using fuzzy-based Monte Carlo simulation

DOI:10.36680/j.itcon.2020.005
submitted:July 2019
revised:November 2019
published:February 2020
editor(s):Turk Ž.
authors:Osama Moselhi, Professor,
Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Qc, Canada
moselhi@encs.concordia.ca

Mohammadjavad Arabpour Roghabadi, PhD Candidate,
Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Qc, Canada
mohammadjavad.arabpour@concordia.ca
summary:Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.
keywords:Risk quantification, Contingency estimation, Correlation, Monte Carlo Simulation (MCS), Fuzzy sets theory
full text: (PDF file, 0.509 MB)
citation:Moselhi O, Roghabadi M A (2020). Risk quantification using fuzzy-based Monte Carlo simulation, ITcon Vol. 25, pg. 87-98, https://doi.org/10.36680/j.itcon.2020.005