Digital Twin Technology for Cost Optimization and Risk Reduction in Infrastructure Projects

Digital Twin (DT) technology is increasingly recognized as a strategic tool for improving cost efficiency and risk management in infrastructure projects through real-time data integration, predictive analytics, and lifecycle optimization. As global infrastructure systems continue to experience cost overruns, resource inefficiencies, and elevated risk exposure, DT provides a dynamic digital–physical interface that enhances transparency, operational accuracy, and decision-making across all project phases. This study examines the impact of DT adoption on cost optimization and risk reduction through multi-level integration with Building Information Modeling (BIM), the Internet of Things (IoT), and Artificial Intelligence (AI). A mixed-method research design was applied, combining a systematic literature review, comparative case analysis, and quantitative modeling based on 12 international infrastructure projects implemented between 2020 and 2024 in the transport, energy, and civil engineering sectors. The results indicate that advanced DT implementation can reduce lifecycle costs by up to 25% and operational risk exposure by approximately 30%, depending on digital maturity, interoperability, and data governance quality. Projects utilizing predictive analytics and sensor-based monitoring also demonstrated notable improvements in maintenance efficiency, safety performance, and environmental outcomes. The study proposes an empirical DT cost–risk evaluation framework tailored to developing economies, offering practical guidance for policymakers and infrastructure managers in assessing digital investment efficiency and sustainability outcomes.