Digital Twins and Artificial Intelligence for Sustainable Infrastructure Management: A Lifecycle Review of Performance Evaluation and Emerging Technologies in the Built Environment

This project framework looks at how digital twins and artificial intelligence (AI) can be used throughout the whole life cycle of sustainable infrastructure—from design and construction to operation and deconstruction. It brings together real‑world data from each phase to compare hybrid models that mix physics‑based simulations with data‑driven methods against models that rely only on data. The main topics are: – Very accurate energy forecasting – Real‑time monitoring of structural health – Optimizing predictive maintenance The methodology shows how to fuse data from many sources. It combines physical simulation models with machine learning algorithms to spot structural problems early and to allocate resources more efficiently. Students will work with performance

metrics such as the coefficient of determination for energy forecasts and the percentage of cost reduction achieved in maintenance scheduling. The framework also covers the shift from short‑term simulations to long‑term physical deployments, testing how well AI‑driven digital twins scale and how reliable they are. Overall, this guide helps develop scalable, sustainable digital twin solutions for the built environment, tackles current gaps in real‑world validation, and offers a clear way to evaluate performance across the entire infrastructure lifecycle.

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