Bridging geotechnical engineering and Interpretable AI to secure the infrastructure of tomorrow.
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Our Global Consortium
Geotechnical engineering faces three inherent challenges: uncertainty, heterogeneity, and nonlinearity. The compounding effects of climate change exacerbate these, impeding traditional approaches in accurately predicting geomaterial behaviour.
GRID (Geotechnical Resilience through Intelligent Design) advocates for a pioneering approach to fill this knowledge gap, integrating physics and machine learning to design resilient infrastructure and conduct effective risk assessments.
Integrating PINNs and GenAI into standard geotechnical workflows.
Mitigating risks from geohazards and environmental uncertainty.
Milestones, events, and latest breakthroughs from the GRID consortium.
GRID researcher Zhongqiang Liu from the Norwegian Geotechnical Institute (NGI) has been seconded to The Hong Kong Polytechnic University to attend the 5th International Symposium on Machine Learning & Big Data in Geoscience (5ISMLG), held 10–13 May 2026 at HKUST.
5ISMLG is the flagship conference series of the ISSMGE TC309 on Machine Learning, serving as a premier platform for researchers and practitioners to exchange innovative ideas on machine learning and big data analytics in geoscience and geoengineering. Zhongqiang's secondment strengthens the collaboration between European and Asian partners within the GRID consortium.
Predict soil grain size distributions from images! $500 in prizes — directly stemming from GRID's WP5 research.
Enter on Kaggle
GRID project coordinator Enrico Soranzo took part in the Long Night of Research at BOKU, showcasing the use of AI to predict the particle size distribution of soils through smartphone pictures.
Tiancheng Wang (TUM) and Yianming Xu (BOKU) began their academic exchange at The Hong Kong Polytechnic University, hosted by Prof. Zhen-Yu Yin.
This secondment strengthens collaboration between European and Asian partners, advancing research at the intersection of geotechnical engineering and interpretable AI.
deepsoil.at revolutionizes geotechnical soil analysis using AI - the grai technology determines soil particle size distribution directly from smartphone images.
Visit deepsoil.at
BOKU welcomed ETS representatives Federico Foria, Mario Calicchio, and Francesco Panico for an intensive collaboration meeting. Enrico Soranzo delivered ML training to the ETS team.
A highlight was the meeting with Prof. Konrad Bergmeister, former CEO of the BBT project, discussing WP5 applications for tunnelling.
We're excited to share the newest edition of the GRID Newsletter including Yanjie Song's PINNs for PDE solution and the GRID student contest updates.
Read PDF
Leeds fellow Yanjie Song presented "Loss-Attentional and Time-Attentional AI Model for Solving PDE Problems," a highlight of WP4.
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Our multi-disciplinary approach is organized into specialized work packages focusing on distinct aspects of geotechnical resilience.
Curating and standardising high-quality geotechnical data for machine learning.
Probabilistic modeling and variability analysis in soil properties.
Synthetic data generation and design optimization using GenAI.
Integrating physical laws into neural networks for geotechnical solution.
Application to tunnels, foundations, and geohazard mitigation.
Developing transparent AI models for engineering decisions.
Open access to our research papers, datasets, and interactive tools.
Listen to AI-generated summaries of our research papers via NotebookLM.
Explore our research posters and technical presentations shared at international conferences.
Comprehensive overview of the GRID project objectives and early findings presented at GMK 2025.
View PosterPortable smartphone-based soil particle size distribution using interpretable AI.
View PresentationIntegrating artificial intelligence with physical principles to enhance safety in pile driving operations.
View PresentationInnovative mobile solutions for soil grain size distribution analysis using digital imaging.
View PresentationAccelerating 3D consolidation simulations using Physics-Informed Neural Networks.
View PresentationPhysics-informed approaches to overcome challenges in geotechnical modeling with limited data.
View PresentationInvestigating the microscopic structural changes that lead to geotechnical failures.
View PresentationDownload official project materials, newsletters, and competition details.
Participate in the global Machine Learning contest for soil shear prediction. Winners announced in Graz, Oct 2026.
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