Probabilistic risk assessment framework for cost overruns predictions in infrastructure projects using randomized simulations

This paper introduces PRIMoS (Probabilistic Risk matrix Integration with MOnte carlo Simulation), an advanced computational framework that enhances cost overrun risk assessment and uncertainty quantification in infrastructure project management. PRIMoS is an innovative Bayesian Monte Carlo simulation framework integrated with a probabilistic risk matrix, providing comprehensive cost risk analysis. The proposed framework simultaneously addresses both cost uncertainties and time uncertainties, the latter through discount rate assessment, extending beyond traditional cost-focused approaches. PRIMoS employs a novel method to define risk magnitude (RM) levels for all project components, enabling adaptive probability distributions for Monte Carlo inputs. This approach allows for the capture of specific cost-related interdependencies and evolving risk patterns within the financial aspects of the project lifecycle. The framework's efficacy was demonstrated through application to a large infrastructure project, showcasing its ability to provide more accurate and detailed cost overrun forecasts compared to conventional methods. The proposed model improved cost estimation accuracy by predicting an increase in contingencies, thereby reducing the estimation error to less than 5%. PRIMoS offers a powerful tool for proactive risk management and informed decision-making in large-scale infrastructure development.