Monte Carlo Simulation for Construction Project Cost Risk Analysis: A Review of Probabilistic Estimation, Contingency Planning and Risk-Driver Modelling

This review examined Monte Carlo Simulation and related probabilistic methods for construction project cost-risk analysis. The synthesis shows that deterministic BOQ estimates remain essential but are incomplete as decision tools because they do not communicate cost range, overrun probability or confidence-based contingency. MCS addresses this weakness by transforming item-level uncertainty into a total cost distribution from which mean cost, standard deviation, P50, P80, P90 and risk allowances can be calculated. The literature confirms that MCS is practical for construction because it can be implemented using existing BOQ structures and understandable distribution assumptions. Triangular distributions are particularly useful when historical data are limited, although their assumptions should be documented carefully. For advanced applications, cost-risk models should incorporate dependence between cost items, schedule-cost interaction, fuzzy or expert uncertainty, and dynamic updating through earned-value or procurement data. The source document reinforces the practical importance of this review. Its three building-project simulations showed high overrun probabilities and the repeated dominance of RCC and finishing-related items as cost-risk drivers. This supports the central conclusion that cost contingency should not be treated as a fixed percentage only; it should be selected from probability-based outputs and supported by item-wise risk-driver control. Future research should therefore focus on integrated, data-updated and decision-oriented cost-risk models that are simple enough for routine construction practice but rigorous enough for academic and professional acceptance.

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