Digital Twin–Based Decision Support For Cost Optimization And Risk Management In Infrastructure Systems

Infrastructure systems face increasing challenges related to cost overruns, operational uncertainty, and riskexposure throughout their lifecycle. Digital twin technology offers new opportunities to support data-driven decisionmakingand improve infrastructure performance. This study aims to develop and evaluate a digital twin-based decisionsupport framework for cost optimization and risk management in infrastructure systems. The methodology integratessystem modeling, real-time data synchronization, and scenario-based simulation within a digital twin environment.Quantitative methods, including lifecycle cost analysis and risk assessment indicators, are applied to compare alternativeinfrastructure management strategies. A case study of infrastructure assets in Eastern Uzbekistan is used to validate theproposed framework. The results indicate that the digital twin-based approach enables a reduction in projected lifecyclecosts by 15-22% and a decrease in risk exposure by up to 18% compared to conventional management methods.Sensitivity analysis confirms the robustness of the framework under varying levels of uncertainty. The practical value ofthis research lies in providing infrastructure managers and policymakers with a scalable decision-support tool to improveinvestment planning, operational efficiency, and risk-informed decision-making