Abstract
The construction industry faces pressure to improve sustainability, but a critical gap persists between conceptual Digital Twin (DT) frameworks and the empirically validated, operational workflows needed for material design. To bridge this gap, this research proposes and empirically evaluates a tangible, end-to-end Industry 4.0 workflow. This study serves as a proof-of-concept, demonstrating how a DT can be developed from a theoretical concept into a practical optimization engine for material design. Our methodology operationalizes this blueprint by utilizing a public dataset as a proxy for digitalization, developing a deep neural network (DNN) as the high-fidelity virtual twin, and benchmarking it against four classical machine learning (ML) models to predict concrete compressive strength. The results provide a strong performance benchmark for this approach: the deep learning-based Digital Twin achieves high predictive fidelity with an R² of 0.9011 and a Root Mean Squared Error (RMSE) of 5.09 Megapascals (MPa) on the held-out test set. In stark contrast, baseline linear models achieved an R-squared (R²) of only 0.632, demonstrating that the complexity of our proposed workflow is essential for capturing non-linear material interactions. This work’s primary contribution is a reproducible blueprint for applying predictive analytics in sustainable construction, enabling the data-driven design of high-performance, low-carbon concrete mixes while significantly reducing the need for physical experimentation.