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.
Project coordinator Enrico Soranzo (BOKU) was seconded to UniSafe, hosted by Prof. Paolo Simonini, the local coordinator. During the visit they also met with the team from the University of Padua, led by Prof. Francesca Ceccato.
During the secondment they worked together on developing AI agents for geotechnical engineering and on collecting data samples for the machine-learning classification of soils through computer vision — directly feeding GRID's WP5 research.
Many GRIDders came to BOKU and were seconded to HDAnalytics, also attending the ICSMGE conference. The group included Bilotta & Falcone (UNINA), Foria & Panico (ETS), Ceccato (UniPD), Mühlmann & Rieser (HDAnalytics), and Zhenyu Yin (PolyU) at BOKU next to our Totem.
A Grant Agreement Amendment was also finalized, welcoming UniPD, UniSafe, SmartG, HPC, OsloMet and Tongji as new partners.
D2.1: Report on statistical models with guideline for uncertainty quantification and risk management strategies, authored by Tiancheng Wang (TUM) with internal review by Enrico Soranzo (BOKU) and Wei Yan (TUM). The methodological foundation for WP2 — Uncertainty.
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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.
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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.
A poster by Enrico Soranzo (BOKU) on an AI-assisted grading workflow that gives students automated, interpretable feedback on their submissions, supporting self-evaluation and formative learning while keeping the educator in the loop.
View PosterA poster by Enrico Soranzo (BOKU) exploring an AI-driven framework for the reliability analysis of soil slopes reinforced by plant roots, combining interpretable machine learning with vegetated-slope stability modelling.
View PosterComprehensive 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.
Predict soil grain size distributions from images! $500 in prizes — directly stemming from GRID's WP5 research.
World-class experts from leading research institutions and industry leaders.
BOKU
Uni Napoli
NGI
TUM
LNEC
ETS
UNSJ
Leeds
GGU
UCC
PolyU
HDAnalytics
civilserve
UniPD
Smart-G
UniSafe
HPC
OsloMet
Tongji
