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Making geotechnical engineering smarter

AutoGeo Lab ('Autonomous Geotechnics Lab') is a research group led by Dr. Stephen Suryasentana at the University of Strathclyde. Our research is focused on developing innovative offshore geotechnics solutions to prepare for climate change and the autonomous age. This includes developing intelligent modelling tools and novel sensor-based solutions to help engineers improve their decision-making in offshore ground modelling, site investigation planning, foundation design optimisation and installation risk management. At AutoGeo Lab, we believe that the time is right to rethink the geotechnical design process using a new paradigm and a more modern toolkit, with the goal of helping engineers design more cost-effective, reliable, low-carbon and eco-friendly foundations.

Research Themes

Suction caisson/bucket

  • Development of improved design methods
  • AI-driven foundation monitoring using novel sensors

Automated ground modelling

  • Geophysical & geotechnical data fusion
  • Automated soil layering identification
  • Gaussian processes
  • Markov chain Monte Carlo (MCMC)
  • Multi-fidelity data fusion

Offshore soil-structure interaction

  • Development of simplified design models for offshore foundations
  • Large deformation finite element modelling
  • Convex constitutive modelling

Principal Investigator

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Dr. Stephen Suryasentana is a Lecturer (Chancellor's Fellow), or US-equivalent: tenure-track Assistant Professor, at the University of Strathclyde, and the leader of AutoGeo Lab. Before joining Strathclyde, he was a Junior Research Fellow at Wolfson College, Oxford. Dr. Suryasentana completed a DPhil (PhD) in Engineering Science from University of Oxford, a BEng in Civil Engineering (First Class Honours; Faculty of Engineering, Computing and Mathematics Medal) from University of Western Australia and a BBA from National University of Singapore. His research has been awarded prizes from the Society for Underwater Technology and the Australian Geomechanics Society. Beyond academia, Dr. Suryasentana also has direct working experience in the offshore and mining industry.

Dr. Stephen Suryasentana

Members

Current Vacancies

We are currently recruiting PhD students to join the lab on fully-funded scholarships. Please get in touch if you are interested in the following vacancies or want to know more:
  • Starting in Oct 2021: A funded PhD position for development of novel ways to integrate geophysical and geotechnical data for automated ground modelling of offshore wind farms.
  • Starting in Oct 2021: A funded PhD position for development of novel machine learning-based solutions for offshore wind foundations monitoring.
  • PhD Students

    • Benjamin Williams (PhD, expected 2025) - Development of novel machine learning-based solutions for offshore wind foundations monitoring
    • Chai Yuxuan (PhD, expected 2026) - Applications of generative AI in geotechnical engineering
    • Anjana Chandran (PhD, expected 2027) - Influence of pressure cycling on post-installation performance of suction caisson foundations

    Alumni

    • Andrew Mactavish (Strathclyde, MEng 2023): Statistical Analysis of Soil Profiles Representative of UK Offshore Wind Sites.
    • Lewis Rands (Strathclyde, MEng 2023): Assessing the suitability of conventional thermal conductivity testing when compared to back-analysed cable thermal measurements for offshore wind farms.
    • Ilona Ganczarek (Strathclyde, MSc 2022): Numerical Investigation of the Effect of Gapping on the Natural Frequency of Suction Caissons Under Lateral Loading.
    • Paul Clark (Strathclyde, MSc 2022): Comparison of CPT-based design methods for laterally loaded piles using back-analysis of field data.
    • Murray McVicar (Strathclyde, MEng 2021): A comparative study of large deformation finite element analysis for suction caisson installation simulation.
    • Manvir Supra (Strathclyde, MEng 2021): Bayesian back-analysis of pile test data.
    • Xianqi Jiang (Oxford, MEng 2020): Automatic identification of soil stratification using machine learning (co-supervised with Brian Sheil)
    • Matt Waters (Oxford, MEng 2020): Data-driven predictions of foundation stiffness on arbitrary, multi-layered grounds (co-supervised with Harvey Burd)

    Publications

    1. 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.
    2. 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.
    3. Suryasentana, S. K., Burd, H. J., Byrne, B. W. & Shonberg, A. (2023). Small-strain, non-linear elastic Winkler model for uniaxial loading of suction caisson foundations. Géotechnique Letters 13(4):1-26.
    4. Buckley, R., Chen, Y. M., Sheil, B., Suryasentana, S., Xu, D., Doherty, J., & Randolph, M. (2023). Bayesian Optimization for CPT-Based Prediction of Impact Pile Drivability. Journal of Geotechnical and Geoenvironmental Engineering, 149(11), 04023100.
    5. 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.
    6. [ Download Code ] 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)
    7. Suryasentana, S. K. & Houlsby, G. T. (2022) A Convex Modular Modelling (CMM) framework for developing thermodynamically consistent constitutive models. Computers and Geotechnics
    8. Sheil, B. B., Suryasentana, S. K., Templeman, J. O., Phillips, B. M., Cheng, W. C., & Zhang, L. (2022). Prediction of pipe-jacking forces using a Bayesian updating approach. Journal of Geotechnical and Geoenvironmental Engineering, 148 (1), 04021173.
    9. 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
    10. Suryasentana, S. K., Burd, H. J., Byrne, B. W. & Shonberg, A. (2020) A Winkler model for suction caisson foundations in homogeneous and non-homogeneous linear elastic soil. Géotechnique
    11. 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 Construction, 1-22.
    12. Sheil, B. B., Suryasentana, S. K. & Cheng, W. C. (2020) An assessment of anomaly detection methods applied to microtunnelling. Journal of Geotechnical and Geoenvironmental Engineering, 146 (9), 04020094.
    13. 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. ISFOG 2020 Conference, Texas.
    14. Suryasentana, S. K., Burd, H. J., & Byrne, B. W. (2019) Automated optimisation of suction caisson foundations using a computationally efficient elastoplastic Winkler model. Coastal Structures 2019 Conference, Hannover.
    15. [ Download Code ] 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.
    16. Suryasentana, S. K., Dunne, H. P., Martin, C. M., Burd, H. J., Byrne, B. W. & Shonberg, A. (2019) Assessment of Numerical Procedures for Determination of Shallow Foundation Failure Envelopes. Géotechnique. 70(1), 60-70.
    17. 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, Vol. 2, 973-980. CRC Press.
    18. 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. Proceedings of the 19th International Conference on Soil Mechanics and Geotechnical Engineering.
    19. 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. Offshore Site Investigation Geotechnics 8th International Conference Proceeding Vol. 554(561), 554-561.
    20. 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.
    21. Suryasentana, S. K. & Lehane, B. M. (2014) Verification of numerically derived CPT based p–y curves for piles in sand. In 3rd International Symposium on Penetration Testing.
    22. Suryasentana, S. K. & Lehane, B. M. (2014) Numerical derivation of CPT-based p–y curves for piles in sand. Géotechnique, 64(3), 186-194.

    Contact

    At AutoGeo Lab, we aim to build a unique culture where researchers and industry partners work together to identify the most important problems, and design rigorous solutions that make a real impact. If you have a potential research topic or industry-facing problem, please get in touch and we can discuss potential pathways of collaboration that work best for you.

    We are disciplined risk-takers willing to push boundaries within the field of geotechnical engineering. We are always looking for talented students to join our growing team and help accelerate our journey to create next-generation solutions that bring together ideas and tools from different scientific disciplines. If you are a UK student who is interested in joining the lab, please get in touch with your research interests, so that we can discuss more about your funding eligibility for internal PhD funding opportunities. Alternatively, there are also external PhD funding opportunities as follows:

    We are firm believers of collaboration and we welcome academic visitors or collaborators who are interested in working with us. If you are a researcher who is interested in collaborating or visiting the lab, please get in touch.

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