Uncertainty Analysis using Probabilistic Estimates in Risk Assessment

Abstract

Monte Carlo Simulation (MCS) was applied as a decision-making model to quantify the level of project risk based on risk factors taken from expert opinions and literature studies. The model classifies the datasets of a construction project into one of the five classes such as tolerable, low, medium, high and intolerable level of risk. As agreed, the probability of risk (RP) and the impact of risk (RI) were selected as the inputs for assessing the level of risk (RL). MCS tools have been widely utilized to deal with the inherent variability in construction systems and is a very useful technique for modeling and analyzing real-world systems. The objective of this paper is to evaluate the use of MCS to quantify project risk. Consequently, risk assessment using MCS to represent risk RP and RI is carried out. Using the Monte Carlo method, the risk on processes (R2), construction error (R4) and process delay (R6), low productivity (R8), quality issues (R12), and technical problem (R13) were evaluated in a LOW-HIGH risk range, while the MEDIUM-HIGH risk was contributed by processes (R1), inexperienced project management team (R3), design errors (R5), construction errors (R7), inexperienced site workers (R9), site accidents (R10). At 95% certainty, the highest risk on the project was contributed by construction error (R7) and delay in delay (R11), with a mean value of 56%, followed by risk on design error, R5 (55.8%), risk on inexperienced workers, R9 (47%), and risk on inexperienced project management team, R3 (46.5%).