A Risk-Informed Digital Twin Framework for Sustainable Construction Scheduling and Carbon Optimization Under Uncertainty

The construction sector accounts for 34% of global energy-related CO2 emissions, yet existing Digital Twin (DT) applications in construction remain largely descriptive, lacking the predictive and prescriptive capabilities required for proactive execution management. This study presents and evaluates, as a simulation-based proof of concept, a risk-informed DT framework integrating a formalized DT state transition model, a dual deep learning prediction engine (Long Short-Term Memory (LSTM) and Transformer), Monte Carlo-based probabilistic carbon quantification for lifecycle modules A1–A5 (S = 10,000), and a risk-adjusted, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimizer incorporating carbon variance, within a closed weekly feedback loop. The framework is evaluated across five European case studies (residential, education, and commercial office; the Netherlands, the UK, and France) anchored to published project data, with primary calibration on a hybrid real–synthetic mid-rise residential building (CS1; 7200 m2, eight stories, the Netherlands) and a fully documented synthetic layer generating the execution records that the published sources do not provide. Under common random number simulation with 50 execution realizations per case, the full framework achieved simultaneous improvements of 5.8–13.2% in expected schedule duration and 10.4–16.4% in expected embodied carbon relative to a static Critical Path Method (CPM) baseline; for CS1, mean intensity fell from 476 to 426 kgCO2e/m2. Component ablation identified the bi-objective optimizer as the dominant contributor, producing an 11.7–20.2% carbon increase when removed, and the selected Pareto solution was invariant to the risk-aversion parameter λ over [0, 1], indicating that risk adjustment functions as a conservatism margin on the reported carbon target rather than a decision-altering preference. The framework advances construction DTs from descriptive monitoring tools toward risk-informed decision support, offering a transparent and reproducible benchmark that advances data-driven sustainability in construction scheduling and embodied carbon management.