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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. We leverage cutting-edge probabilistic machine learning techniques to develop intelligent modelling tools to help geotechnical engineers design more efficient foundations (especially for offshore renewable structures such as offshore wind turbines). 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 designing more reliable foundations at a much lower cost.

Research

Geotechnical design is fundamentally a data-driven problem. Naturally, it starts with the raw ground data, but this data on its own offers limited value; it has to be effectively interpreted to advance the geotechnical design process. At AutoGeo Lab, we are enabling better, smarter and highly reproducible insights throughout the geotechnical design value chain.

Traditional Geotechnics
Traditional geotechnical engineering tends to be manually-driven, time-consuming and ad hoc. Furthermore, the information flow is typically one-way, which does not provide opportunities to improve the modelling decisions.
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Autonomous Geotechnics
Autonomous geotechnical engineering aims to be self-driven, automated, fast and reproducible. Furthermore, the information flow is two-way, which provides opportunities to improve the modelling decisions.
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Principal Investigator

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Dr. Stephen Suryasentana is a Chancellor's Fellow (Lecturer 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 the National University of Singapore. He was previously a visiting student at the University of Illinois at Urbana Champaign, and 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

Former Members or Visitors

  • 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., Sheil B. B., Lawler, M., Jiang, X., Lehane, B. M. (2020) Automated CPT-based soil layering identification using offline and online Bayesian changepoint detection. (under review)
  2. 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. (accepted)
  3. 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 (accepted)
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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 both engineering science and machine learning. If you are a prospective 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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