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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 coming digitalisation 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
  • Smart sensors to derisk installation
  • Novel geochemistry solutions to lower carbon footprint

Automated ground modelling

  • Geophysical & geotechnical data fusion
  • Automated soil layering identification
  • Gaussian processes
  • Markov chain Monte Carlo (MCMC)
  • Uncertainty Quantification

Offshore soil-structure interaction

  • Development of simplified design methods
  • Large deformation finite element modelling
  • Thermodynamically-consistent soil constitutive modelling

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 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 and sensor-based solutions for offshore wind foundations monitoring.

PhD Students

  • Christos Antonopoulos (Strathclyde, PhD) - Effects of scour on soil-structure interaction of bridges (co-supervised with Enrico Tubaldi)

Alumni

  • 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. (2020) A Winkler model for suction caisson foundations in homogeneous and non-homogeneous linear elastic soil. Géotechnique (accepted)
  2. 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.
  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, 146 (9), 04020094.
  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. [ 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.
  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 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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