Integrating Deterministic and Monte Carlo Approaches to the Risk Reduction Index for Sustainable Urban Construction Projects

Feby Kartika Sari (Department of Civil Engineering, Faculty of Civil Engineering and Planning, Universitas Trisakti, Jakarta, Indonesia)
Muhammad Sapto Nugroho (Department of Civil Engineering, Faculty of Civil Engineering and Planning, Universitas Trisakti, Jakarta, Indonesia)
Bambang Endro Yuwono (Department of Civil Engineering, Faculty of Civil Engineering and Planning, Universitas Trisakti, Jakarta, Indonesia)
Ryan Faza Prasetyo Prasetyo (Department of Civil Engineering, Faculty of Civil Engineering and Planning, Universitas Trisakti, Jakarta, Indonesia)
Citra Mira Dewi Bonastria (Institute for Transport Studies, Faculty of Environment, University of Leeds, Leeds, West Yorkshire, United Kingdom)
Olgavian Ade Saputra (School of GeoSciences, College of Science & Engineering, The University of Edinburgh, Edinburgh, Scotland, United Kingdom)

INDONESIAN JOURNAL OF URBAN AND ENVIRONMENTAL TECHNOLOGY
VOLUME 9, NUMBER 1, APRIL 2026

Aim: This study aims to evaluate the effectiveness of risk mitigation strategies in construction projects by integrating deterministic and probabilistic analyses within the Risk Reduction Index (RRI) framework to support sustainable urban development. Methodology and results: Deterministic RRI calculations were first applied to assess changes in risk exposure before and after mitigation measures. This was followed by probabilistic analysis using Monte Carlo simulation to account for uncertainty and variability in risk likelihood and impact. The results indicate substantial differences between the two approaches. Bad Weather (R4) demonstrated consistently effective mitigation across both methods. However, technical risks such as Quality (R11) showed a higher probability of mitigation failure under probabilistic analysis. Significant discrepancies were also observed for Land Acquisition (R1), Project Delays (R3), and Non-finalized Design (R5), where deterministic analysis indicated limited risk, while Monte Carlo simulation revealed negative mean RRI values and high failure probabilities. Conclusion, Significance, and Impact: The study concludes that combining deterministic and probabilistic RRI analyses offers a more robust and realistic assessment of mitigation performance. This integrated approach enhances the identification of hidden vulnerabilities, supports more informed decision-making, and contributes to infrastructure resilience and transparent environmental risk management in line with SDG 9 and SDG 11.