Project description
New system offers a reliable approach to managing tunnelling projects
Predicting ground behaviour during tunnel excavation is essential for safe and efficient underground construction. However, complex interactions between soil, pore pressure and tunnelling machinery evolve dynamically, making real-time prediction difficult. Traditional numerical simulations are accurate but computationally expensive, while conventional machine learning models often struggle to generalise under changing conditions. Supported by the Marie Skłodowska-Curie Actions programme, the PIMEX project will develop a real-time modular framework combining physics-informed models with advanced artificial intelligence. By integrating physical laws with machine learning, PIMEX aims to improve prediction accuracy, support decision-making, and enhance safety and efficiency in underground infrastructure development.
Objective
The growing demand for tunnel excavation is directly linked to urbanization, with over 75% of Europe’s population already living in cities and this figure projected to exceed 80% by 2050. As urban areas expand, the need to extend infrastructure underground becomes critical. Subsurface development, however, introduces significant geotechnical risks, where even minor delays or failures can result in severe economic and societal consequences. Real-time predictive modelling tools are essential to ensure safety and resilience.
Despite advances in modelling, predicting ground behaviour in real time remains a major challenge. Excavation involves coupled processes, soil displacement, 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 predictive system for underground excavation, bridging physics and data-driven methods to reduce risks, optimize operations, and contribute to safer, more cost-effective infrastructure delivery under evolving conditions.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- engineering and technology environmental engineering geotechnics
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences mathematics applied mathematics numerical analysis
- natural sciences computer and information sciences artificial intelligence computational intelligence
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
DH1 3LE DURHAM
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.