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PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING
Edited by: B.H.V. Topping
Modelling Uncertainty for Civil Engineering Projects by Questioning the Inputs
M. El-Cheikh1, A.H.C. Chan2 and J. Lamb2
1Department of Transport, Ove ARUP, Solihull, United Kingdom
M. El-Cheikh, A.H.C. Chan, J. Lamb, "Modelling Uncertainty for Civil Engineering Projects by Questioning the Inputs", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 65, 2007. doi:10.4203/ccp.86.65
Keywords: Monte Carlo Simulation, uncertainty, fuzzy sets theory, risk analysis, risk quantitative analysis.
Modelling uncertainty is one of the vital requirements which could guarantee better planning and constructing for many civil engineering projects. Addressing the needs of construction projects is one of the main aspects that an analysis study of risk has to address as clearly and as early as possible. As a result tackling the problem relating uncertainties, modelling what is expected or not expected, are of high priority for both project analysts and researchers . However, many approaches have been proposed. The main models used for the present paper are the Monte Carlo simulation and the fuzzy sets applications. The findings of the research concluded that by the use of the probability theory and Monte Carlo simulation applications in the civil engineering project, it had led in many cases to optimistic results, an issue that does not satisfy the client requirements and does not challenge the project managers, planners or estimators . The findings also addressed the fact that the estimator needs to be more certain about his estimations if Monte Carlo simulation is to be applied. The authors argue that applying the fuzzy sets theory, in addressing uncertainty, will minimize the range of the uncertainty. This will lead to better outputs than those obtained from the Monte Carlo simulation as it will improve the scheduled plan and the estimated project costs.
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