AutoGeo Lab

Research intelligence for geotechnics

Geotechnical research intelligence for offshore infrastructure.

AutoGeo Lab turns foundation mechanics, CPT data, uncertainty modelling, and machine learning into sharper methods for piles, suction caissons, tunnelling, and offshore ground decisions.

Research profile Publications

Research architecture

Four research lines powering the lab.

Technical cutaway of an offshore pile foundation with CPT traces and p-y response curves 01

CPT-informed foundation response

Numerical derivation and verification of CPT-based p-y curves for piles in sand, updated formulations for laterally loaded piles, and drivability prediction.

Suction caisson foundation cutaway with layered soil, stiffness springs, and finite element contours 02

Suction caisson design models

Winkler models, six-degree-of-freedom stiffness, elastoplastic caisson response, and combined loading formulations for offshore foundation design.

Soil samples, CPT traces, finite element mesh, and neural network data fusion visualization 03

Geotechnical AI and data fusion

Physics-informed machine learning, multi-fidelity fusion, soil classification, automatic boundary segmentation, and AI transformation in geotechnics.

Tunnel boring machine cutaway with sensor streams and predictive data visualization 04

Construction intelligence

Machine learning for tunnelling operations, anomaly detection in microtunnelling, Bayesian updating for pipe-jacking forces, and data-led operational forecasting.

Research projects

Current projects moving geotechnics toward intelligent infrastructure.

Generative and agentic AI for 3D geological modelling in tunnelling
Generative AI and tunnelling

Generative and Agentic AI for 3D Geological Modelling in Tunnelling

This project uses AI to build realistic 3D models of the ground for tunnelling. It combines different types of geological data with modern AI tools to create clear, visual ground models.

The project also develops AI assistants that let users explore these models using simple, everyday language. These tools help identify potential ground risks and automatically generate easy-to-understand reports. No specialist knowledge is needed.

Better understanding of the ground leads to safer and more efficient infrastructure.

Pressure cycling control around a suction caisson during offshore installation
Suction caisson installation

Pressure cycling control of flow and soil deformation during suction caisson installation and Pressure cycling effects on post-installation behaviour of suction caissons.

This project investigates pressure cycling control during installation and the effects of pressure cycling on post-installation suction caisson behaviour.

Digital twin monitoring of suction caisson installation with AI sensors for soil plug hazards
Digital twin monitoring

Digital twin risk monitoring of suction caisson installation under soil plug hazards, including AI sensor development for soil plug hazard monitoring for caisson installation.

This project develops digital twin risk monitoring for suction caisson installation under soil plug hazards, including AI sensor development for real-time soil plug hazard monitoring.

Intelligent driveability forecasting for offshore wind monopile foundations
Offshore wind foundations

Intelligent driveability forecasting for offshore wind monopile foundations.

This project develops Bayesian methods to enhance pile driveability response predictions and facilitate the planning of offshore wind foundations.

Research operating model

Mechanics first. Data second. Decisions always.

The lab follows a consistent pipeline: start from measurable ground data, encode soil-structure mechanics, calibrate with statistics, then deploy fast models that engineers can use under uncertainty.

Input

CPT, field records, tunnel operations, and site data

Observed signals become the starting point for model calibration and uncertainty reduction.

Model

Finite elements, Winkler models, Bayesian updates, and neural networks

Different model fidelities are selected for the level of confidence and speed required.

Output

Design curves, stiffness estimates, boundaries, classifications, and forecasts

Research outputs are framed as usable engineering decisions, not just model scores.

Team

Research group.

Dr Stephen Suryasentana Principal investigator

Dr Stephen Suryasentana

Senior Lecturer, University of Strathclyde

Research profile

Dr Stephen Suryasentana is a Senior Lecturer at the University of Strathclyde and leads the AutoGeo Lab, developing AI-enabled methods for the digital transformation of geotechnical engineering. His research focuses on the digital lifecycle of foundation systems, from ground modelling and soil–structure interaction to design, installation, and monitoring, with particular emphasis on offshore wind infrastructure.

He holds a DPhil in Engineering Science from the University of Oxford, where he was later elected to a Junior Research Fellowship at Wolfson College. He also holds a First Class Honours BEng in Civil Engineering from the University of Western Australia, where he received the Faculty Medal, and a BBA from the National University of Singapore.

Dr Suryasentana has pioneered multi-fidelity data-fusion methods for estimating foundation–soil stiffness in wind turbines and advanced machine learning applications in ground modelling and foundation design. His work has received awards from the Society for Underwater Technology and the Australian Geomechanics Society. Since joining Strathclyde in 2020, he has secured PI funding from the Royal Society, EPSRC, the Energy Technology Partnership, and industry partners including Ørsted, ESB and SSE Renewables.

PhD students

Doctoral researchers.

Benjamin Williams Machine learning-based offshore foundation monitoring
Chai Yuxuan Generative AI in civil engineering
Anjana Chandran Pressure cycling and suction caisson performance
Isabel Stansbury Anchor foundations for floating wind farms
Lloyd Inglis Drag embedment anchors for offshore mooring systems

Postdocs and Research Assistants

Postdocs and Research Assistants.

2026 Dr Mohammad Mubashar
Generative and Agentic AI for 3D Geological Modelling in Tunnelling
2026 Dr Namgwon Kim
Generative and Agentic AI for 3D Geological Modelling in Tunnelling
2025 Shujin Zhou
Pressure cycling control of flow and soil deformation during suction caisson installation

Selected publications

Selected research highlights.

Most cited publication

Numerical derivation of CPT-based p–y curves for piles in sand

Suryasentana, S. K., & Lehane, B. M. (2014). Géotechnique 64 (3), 186-194.

Most recent publications
  • Zhou, X., Sheil, B., Suryasentana, S., & Shi, P. (2026). Graph attention neural network for subsurface stratigraphy on spatial and feature level using multiple-source sparse exploration data.
  • Suryasentana, S., & Stuyts, B. (2026). Machine Learning in Offshore Geotechnical Engineering.
  • Sheil, B., Anagnostopoulos, C., Buckley, R., Ciantia, M. O., Febrianto, E., Fu, J., et al. (2026). Artificial intelligence transformations in geotechnics: progress, challenges and future enablers.

Publication list

Bibliography

Full-text access Download available publication versions on ResearchGate
  1. 2026
    Zhou, X., Sheil, B., Suryasentana, S., & Shi, P. (2026). Graph attention neural network for subsurface stratigraphy on spatial and feature level using multiple-source sparse exploration data. Advanced Engineering Informatics 70, 104108.
  2. 2026
    Suryasentana, S., & Stuyts, B. (2026). Machine Learning in Offshore Geotechnical Engineering. Machine Learning for Data-Centric Geotechnics.
  3. 2026
    Sheil, B., Anagnostopoulos, C., Buckley, R., Ciantia, M. O., Febrianto, E., Fu, J., et al. (2026). Artificial intelligence transformations in geotechnics: progress, challenges and future enablers. Computers and Geotechnics 189, 107604.
  4. 2025
    Kamas, I., Suryasentana, S. K., Burd, H. J., & Byrne, B. W. (2025). Data-driven 1D design model for monotonic lateral loading of monopile foundations. MethodsX, 103738.
  5. 2025
    Yuan, B., Choo, C. S., Yeo, L. Y., Wang, Y., Yang, Z., Guan, Q., Suryasentana, S., et al. (2025). Physics-informed machine learning in geotechnical engineering: a direction paper. Geomechanics and Geoengineering 20 (5), 1128-1159.
  6. 2025
    Suryasentana, S. K., O’Boyle, B. F., Davidson, J., Bartkus, S., Schroeder, F., et al. (2025). Enhancing CPT-based suction caisson penetration design: insights from back-analysis of large-scale field installation data. Journal of Geotechnical and Geoenvironmental Engineering 151 (7), 04025053.
  7. 2025
    Zhou, X., Sheil, B., Suryasentana, S., & Shi, P. (2025). Uncertainty‐guided U‐Net for soil boundary segmentation using Monte Carlo dropout. Computer‐Aided Civil and Infrastructure Engineering 40 (10), 1370-1387.
  8. 2024
    Zhou, X., Sheil, B., Suryasentana, S., & Shi, P. (2024). Multi-fidelity fusion for soil classification via LSTM and multi-head self-attention CNN model. Advanced Engineering Informatics 62, 102655.
  9. 2024
    Suryasentana, S. K., Sheil, B. B., & Stuyts, B. (2024). Multifidelity data fusion for the estimation of static stiffness of suction caisson foundations in layered soil. Journal of Geotechnical and Geoenvironmental Engineering 150 (8), 04024066.
  10. 2024
    Suryasentana, S. K., Sheil, B. B., & Stuyts, B. (2024). Practical approach for data-efficient metamodeling and real-time modeling of monopiles using physics-informed multifidelity data fusion. Journal of Geotechnical and Geoenvironmental Engineering 150 (8), 06024005.
  11. 2024
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2024). Investigation of local soil resistance on suction caissons at capacity in undrained clay under combined loading. Computers and Geotechnics 169, 106241.
  12. 2024
    Suryasentana, S. K., Sheil, B. B., & Lawler, M. (2024). Assessment of Bayesian changepoint detection methods for soil layering identification using cone penetration test data. Geotechnics 4 (2), 382-398.
  13. 2024
    Zhou, X., Shi, P., Sheil, B., & Suryasentana, S. (2024). Knowledge-based U-Net and transfer learning for automatic boundary segmentation. Advanced Engineering Informatics 59, 102243.
  14. 2023
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2023). Small-strain, non-linear elastic Winkler models for uniaxial loading of suction caisson foundations. Géotechnique Letters 13 (4), 170-181.
  15. 2023
    Buckley, R., Chen, Y. M., Sheil, B., Suryasentana, S., Xu, D., Doherty, J., et al. (2023). Bayesian optimization for CPT-based prediction of impact pile drivability. Journal of Geotechnical and Geoenvironmental Engineering 149 (11), 04023100.
  16. 2023
    Suryasentana, S. K., & Sheil, B. B. (2023). Demystifying the connections between Gaussian process regression and kriging. 9th International SUT OSIG Conference, 1-8.
  17. 2023
    Suryasentana, S. K., & Sheil, B. B. (2023). Demystifying the connections between Gaussian Process regression and kriging theories. SUT Offshore Site Investigation and Geotechnics, SUT-OSIG-23-236.
  18. 2023
    Buckley, R. M., Chen, Y. M., Sheil, B. B., Suryasentana, S. K., Randolph, M. F., et al. (2023). Improving driveability predictions for offshore piles using Bayesian optimisation. SUT Offshore Site Investigation and Geotechnics, SUT-OSIG-23-231.
  19. 2023
    Williams, B., Suryasentana, S., Perry, M., & Donaldson, K. (2023). Artificial Intelligence (AI) driven 3D point scanner for monitoring soil plug hazards during the installation of suction caisson foundations. SUT Offshore Site Investigation and Geotechnics, SUT-OSIG-23-240.
  20. 2023
    Stuyts, B., & Suryasentana, S. (2023). Applications of data science in offshore geotechnical engineering: state of practice and future perspectives. SUT Offshore Site Investigation and Geotechnics, SUT-OSIG-23-232.
  21. 2023
    Kamas, I., Burd, H. J., Byrne, B. W., & Suryasentana, S. K. (2023). Generation of a general Cowden clay model (GCCM) using a data-driven method. SUT Offshore Site Investigation and Geotechnics, SUT-OSIG-23-192.
  22. 2023
    Buckley, R., Chen, Y., Sheil, B., Suryasentana, S., Randolph, M., & Doherty, J. (2023). Bayesian Optimisation Framework for Robust and Adaptive Driveability Predictions for Offshore Wind Piles.
  23. 2023
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2023). Modulus weighting method for stiffness estimations of suction caissons in layered soils. Géotechnique Letters 13 (2), 1-8.
  24. 2023
    Suryasentana, S. K., Lawler, M., Sheil, B. B., & Lehane, B. M. (2023). Probabilistic soil strata delineation using DPT data and Bayesian changepoint detection. Journal of Geotechnical and Geoenvironmental Engineering 149 (4), 06023001.
  25. 2022
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2022). Modulus weighting method for static stiffness estimations of suction caisson foundations in layered soil conditions.
  26. 2022
    Sheil, B., Buckley, R., & Suryasentana, S. (2022). Intelligent Driveability Forecasting for Offshore Wind Turbine Monopile Foundations (iDRIVE).
  27. 2022
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2022). A Winkler model for suction caisson foundations in homogeneous and non-homogeneous linear elastic soil. Géotechnique 72 (5), 407-423.
  28. 2022
    Suryasentana, S. K., & Mayne, P. W. (2022). Simplified method for the lateral, rotational, and torsional static stiffness of circular footings on a nonhomogeneous elastic half-space based on a work-equivalent framework. Journal of Geotechnical and Geoenvironmental Engineering 148 (2), 04021182.
  29. 2022
    Suryasentana, S. K., & Houlsby, G. T. (2022). A convex modular modelling (CMM) framework for developing thermodynamically consistent constitutive models. Computers and Geotechnics 142, 104506.
  30. 2022
    Sheil, B. B., Suryasentana, S. K., Templeman, J. O., Phillips, B. M., Cheng, W. C., et al. (2022). Prediction of pipe-jacking forces using a Bayesian updating approach. Journal of Geotechnical and Geoenvironmental Engineering 148 (1), 04021173.
  31. 2021
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2021). Automated procedure to derive convex failure envelope formulations for circular surface foundations under six degrees of freedom loading. Computers and Geotechnics 137, 104174.
  32. 2020
    Sheil, B. B., Suryasentana, S. K., Mooney, M. A., & Zhu, H. (2020). Machine learning to inform tunnelling operations: Recent advances and future trends. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and ….
  33. 2020
    Sheil, B. B., Suryasentana, S. K., & Cheng, W. C. (2020). Assessment of anomaly detection methods applied to microtunneling. Journal of Geotechnical and Geoenvironmental Engineering 146 (9), 04020094.
  34. 2020
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., Aghakouchak, A., & Sørensen, T. (2020). Comparison of machine learning models in a data-driven approach for scalable and adaptive design of laterally-loaded monopile foundations. Deep Foundations Institute (DFI).
  35. 2020
    Suryasentana, S. K., Burd, H. J., Byrne, B. W., & Shonberg, A. (2020). A systematic framework for formulating convex failure envelopes in multiple loading dimensions. Géotechnique 70 (4), 343-353.
  36. 2020
    Byrne, B. W., Aghakouchak, A., Buckley, R. M., Burd, H. J., Gengenbach, J., et al. (2020). PICASO: Cyclic lateral loading of offshore wind turbine monopiles. Frontiers in Offshore Geotechnics IV: Proceedings of the 4th International ….
  37. 2020
    Suryasentana, S. K., Dunne, H. P., Martin, C. M., Burd, H. J., Byrne, B. W., et al. (2020). Assessment of numerical procedures for determining shallow foundation failure envelopes. Géotechnique 70 (1), 60-70.
  38. 2019
    Suryasentana, S. K., Byrne, B. W., & Burd, H. J. (2019). Automated optimisation of suction caisson foundations using a computationally efficient elastoplastic Winkler model. Coastal Structures Conference 2019, 932-941.
  39. 2018
    Suryasentana, S. K., Byrne, B. W., Burd, H. J., & Shonberg, A. (2018). An elastoplastic 1D Winkler model for suction caisson foundations under combined loading. Numerical Methods in Geotechnical Engineering IX, Volume 2, 973-980.
  40. 2018
    Suryasentana, S. K. (2018). Time-critical design methods for suction caisson foundations. University of Oxford.
  41. 2017
    Suryasentana, S. K., Byrne, B. W., Burd, H. J., & Shonberg, A. (2017). Weighting functions for the stiffness of circular surface footings on multi-layered non-homogeneous elastic half-spaces under general loading. 19th International Conference on Soil Mechanics and Geotechnical Engineering ….
  42. 2017
    Suryasentana, S. K., Byrne, B. W., Burd, H. J., & Shonberg, A. (2017). Simplified model for the stiffness of suction caisson foundations under 6 dof loading. Society for Underwater Technology.
  43. 2016
    Suryasentana, S. K., & Lehane, B. M. (2016). Updated CPT-based p – y formulation for laterally loaded piles in cohesionless soil under static loading. Géotechnique 66 (6), 445-453.
  44. 2014
    Suryasentana, S., & Lehane, B. M. (2014). Verification of numerically derived CPT based py curves for piles in sand. 3rd International Symposium on Cone Penetration Testing.
  45. 2014
    Suryasentana, S. K., & Lehane, B. M. (2014). Numerical derivation of CPT-based p–y curves for piles in sand. Géotechnique 64 (3), 186-194.
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Contact AutoGeo Lab.

For research enquiries, collaboration discussions, or student supervision, use one of the email addresses in the contact card. The postal address is listed with the contact details.

AutoGeo Lab
Email addresses [email protected] [email protected]
Postal address Department of Civil and Environmental Engineering James Weir Building, Level 5 75 Montrose Street University of Strathclyde G1 1XJ
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