Quantitative Risk Analysis Framework for Cost and Time Estimation in Road Infrastructure Projects

Inaccurate cost and schedule estimations in road infrastructure projects continue to be a critical source of contractual disputes and financial inefficiencies, particularly in developing countries. While quantitative risk analysis (QRA) methods such as Monte Carlo simulation (MCS) and schedule risk analysis (SRA) are well-established in the literature, their practical adoption remains limited in contexts with low technical capacity and limited access to advanced modeling tools. This study addresses this gap by proposing a practical and accessible quantitative risk analysis framework tailored to the needs of professionals with limited expertise in probabilistic techniques. The framework combines MCS and SRA using probability distributions (PERT, triangular, and normal) and was empirically validated through three road projects in Peru. Results indicated substantial reductions in uncertainty, achieving cost contingency estimates between 1.34% and 11% which were significantly lower than documented overruns of up to 32.29%. Schedule contingencies ranged from 28.71% to 91.67%, markedly improving accuracy. The novelty of this research lies in its context-adapted implementation strategy, offering a robust and easily replicable approach for similar infrastructure environments in Latin America and beyond. This contribution bridges the gap between theoretical risk modeling and its practical adoption, thus enhancing the reliability of infrastructure planning under resource-constrained conditions.