No abstract

No abstract

Cost estimation is an important task in construction projects. Since various risk‐factors affect the construction costs, the actual costs generally deviate from the estimated costs in a favourable or an adverse direction. Therefore, not only estimation of the costs but also an analysis of the uncertainty of the estimated costs is required. This requirement gains more importance in projects constrained by money as the main driver. The traditional cost estimation, i.e. predicting the construction costs and simply calculating the total, is deterministic and insufficient. This approach neglects the uncertainty and the correlation effects. A new simulation‐based model—the correlated cost risk analysis model (CCRAM)—is proposed to analyse the construction costs under uncertainty when the costs and risk‐factors are correlated. CCRAM captures the correlation between the costs and risk‐factors indirectly and qualitatively. The efficiency and effectiveness of the model is evaluated through an application of CCRAM and Monte Carlo simulation (MCS) based method using the same hypothetical data. The findings show that CCRAM operates well and produces more consistent results compatible with the theoretical expectancies.Cost modelling, uncertainty, risk management, risk analysis, correlation, simulation,

Purpose – Actual costs frequently deviate from the estimated costs in either favorable or adverse direction in construction projects. Conventional cost evaluation methods do not take the uncertainty and correlation effects into account. In this regard, a simulation-based cost risk analysis model, the Correlated Cost Risk Analysis Model, previously has been proposed to evaluate the uncertainty effect on construction costs in case of correlated costs and correlated risk-factors. The purpose of this paper is to introduce the detailed evaluation of the Cost Risk Analysis Model through scenario and sensitivity analyses. Design/methodology/approach – The evaluation process consists of three scenarios with three sensitivity analyses in each and 28 simulations in total. During applications, the model’s important parameter called the mean proportion coefficient is modified and the user-dependent variables like the risk-factor influence degrees are changed to observe the response of the model to these modifications and to examine the indirect, two-sided and qualitative correlation capturing algorithm of the model. Monte Carlo Simulation is also applied on the same data to compare the results. Findings – The findings have shown that the Correlated Cost Risk Analysis Model is capable of capturing the correlation between the costs and between the risk-factors, and operates in accordance with the theoretical expectancies. Originality/value – Correlated Cost Risk Analysis Model can be preferred as a reliable and practical method by the professionals of the construction sector thanks to its detailed evaluation introduced in this paper.

No abstract