The determination of the risk level to be allocated to construction programme is critical to bottom line outcomes. One of the key elements of the programme for civil projects is the calendar and the number of non working days to be allowed for major uncertainties such as rainfall. If, on review of the rainfall database for the area, a high probability of rain is shown, this uncertainty must be addressed properly and accurately in the programme.

Up until now, Deterministic Analysis has been the most common methodology used to determine the extent of the problem for civil and building construction projects. However it is the lack of risk consideration inherent in Deterministic Analysis that creates the opportunity for improving executive decision making by applying programme risk analysis. The Monte Carlo Simulation (MCS) technique is a method for analyzing uncertainty propagation, where the goal is to determine how random variation, lack of knowledge, or error affects the sensitivity, performance, or reliability of the system that is being modeled.

This review outlines the application of Monte Carlo Simulation methodology to assess the number of specific rainy days in a road construction case study (Ballina Bypass Alliance) in Ballina, north of NSW, Australia.

The paper examines the application of MCS concepts for project programming, the advantages and challenges, and provides practical guidance for such a probabilistic analysis with the application of MCS for managing the high risk of rainfall and optimizing the float at the activity level and the project level.