SOURCE-LINKED INTELLIGENCE

Physics-Informed Modular Digital Twin for Real-Time Prediction in Mechanized Tunnelling

CORDIS · observation · Publication date unknown

ent, pore pressure changes, and complex machine-ground interactions, that evolve dynamically. Traditional numerical methods are accurate but computationally intensive and lack real-time adaptability. Machine learning (ML) approaches are faster but often operate as opaque black boxes, requiring large datasets and offering limited generalizability. To address this gap, this project proposes the Physics-Informed Modular Excavation (PIMEX) framework: a real-time, modular system that integrates physics-based models with advanced ML. By embedding physical laws and constraints within neural network architectures such as PINNs, ConvLSTMs, and GNNs, PIMEX will enable interpretable, predictive simulations of excavation processes under sparse and noisy data. The framework will support real-time control, enhancing safety, efficiency, and decision-making in mechanized tunnelling. Validation will be performed through large-scale infrastructure datasets and international collaborations, ensuring the robustness and transferability of the approach. Ultimately, PIMEX aims to deliver a next-generation

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR