DIGITAL TWIN–BASED DECISION SUPPORT FOR COST OPTIMIZATION AND RISK MANAGEMENT IN INFRASTRUCTURE SYSTEMS
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https://doi.org/10.5281/zenodo.18512647##semicolon##
digital twin; infrastructure systems; cost optimization; risk management; decision support; asset management##article.abstract##
Infrastructure systems face increasing challenges related to cost overruns, operational uncertainty, and risk
exposure throughout their lifecycle. Digital twin technology offers new opportunities to support data-driven decisionmaking
and improve infrastructure performance. This study aims to develop and evaluate a digital twin-based decision
support framework for cost optimization and risk management in infrastructure systems. The methodology integrates
system 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 alternative
infrastructure management strategies. A case study of infrastructure assets in Eastern Uzbekistan is used to validate the
proposed framework. The results indicate that the digital twin-based approach enables a reduction in projected lifecycle
costs 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 of
this research lies in providing infrastructure managers and policymakers with a scalable decision-support tool to improve
investment planning, operational efficiency, and risk-informed decision-making
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