Stochastic risk analysis and cost contingency allocation approach for construction projects applying Monte Carlo simulation

The cost contingency estimation is an essential phase in the risk management, especially when the regime of performance is stochastic. This research proposes a probabilistic model to estimate project cost contingency by considering the fact that any risk can occur on a variety of values in terms of economic impact. The impact of risks on the project is achieved by qualitative analysis through three parameters: schedule, cost, and performance. In addition, a stochastic quantitative analysis has been performed using Monte Carlo Simulation (MCS) with the aim to determine the probability distribution of the contingency cost and the related level of risk coverage. The proposed method has been applied on a construction project of a real life company using @Risk for Excel software. By obtaining the contingency amount for the project, it can be realized that with allocating a determined budget, a specific level of risks can be covered and vice versa. Eventually, the robustness of the result was evaluated by another probability distribution to compare the obtained results.