• Investigation of Suction Caisson Behaviour under Pressure Cycling
  The aim of the PhD project is to develop a better understanding of the pressure cycling procedure and its effect on the caisson penetration behaviour during installation and the caisson in-service performance post-installation, through advanced numerical modelling and experimental testing.
• Development of novel ways to integrate geophysical and geotechnical data for automated ground modelling of offshore wind farms
  The research is aimed at advancing the state-of-the-art in automated ground modelling of offshore wind farms. This project seeks to develop a rigorous, statistical framework to automatically combine geophysical and geotechnical data to improve the quality of the ground model for an offshore wind farm site. This will be achieved using statistical and Bayesian machine learning techniques, including Gaussian process (GP) models. This project will also develop novel algorithms that use the integrated ground model to optimise the planning of new site investigation (SI) to collect more information to improve the quality and reduce the uncertainty of the ground model.
• Novel machine learning-based solutions for offshore wind foundations monitoring
  The goal of this project is to develop novel machine learning-based solutions for offshore wind foundations monitoring. Some of the planned applications include intelligent AI-driven scour and soil heave monitoring.

• Time-critical design methods for suction caisson foundations
  The research is aimed at advancing the state-of-the-art in simplified design methods for suction caisson (or suction bucket) foundations, in collaboration with Ørsted, a leading offshore wind developer. This project seeks to develop a brand new family of simplified design methods that are highly suited for offshore wind applications. This is achieved by combining the best of the Winkler soil modelling framework (speed, simplicity, interpretability) and the best of three-dimensional (3D) finite element modelling (rigor, accuracy, robustness). The outcomes of this project is OxCaisson, a family of fast and accurate Winkler-based design methods for suction caisson foundations that works well in complex loading conditions (including the full six degrees-of-freedom loading in fatigue, serviceability and ultimate limit states) and ground conditions (including multi-layered).
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• Starting in May 2024:A 3-year funded PhD position for investigation of suction caisson behaviour under pressure cycling
• 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.

• Benjamin Williams (PhD, expected 2025) - Development of novel machine learning-based solutions for offshore wind foundations monitoring
• Chai Yuxuan (PhD, expected 2026) - Impact of generative AI in civil engineering
• Anjana Chandran (PhD, expected 2027) - Influence of pressure cycling on post-installation performance of suction caisson foundations
• Isabel Stansbury (PhD, expected 2028) - Optimisation of anchor foundations for floating wind farms

• Euan Donnelly (Strathclyde, MEng 2025): The Evaluation of ChatGPT in Undergraduate Civil Engineering Exams.
• Tresor Tshimbombo (Strathclyde, PhD 2024): Automated Manufacturing of Smart Tunnel Segments.
• Craig Lamb (Strathclyde, MSc 2024): Regional Ground Model of United Kingdom Offshore Wind Farm Sites.
• 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)

1. 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.
2. [ Download Code ]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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. [ 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)
10. Suryasentana, S. K. & Houlsby, G. T. (2022) A Convex Modular Modelling (CMM) framework for developing thermodynamically consistent constitutive models. Computers and Geotechnics
11. 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.
12. 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
13. 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
14. 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.
15. 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.
16. 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.
17. 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.
18. [ 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.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. Suryasentana, S. K. & Lehane, B. M. (2014) Numerical derivation of CPT-based p–y curves for piles in sand. Géotechnique, 64(3), 186-194.

• 2019: Automated optimisation of caisson foundations using a computationally efficient elastoplastic Winkler model. Coastal Structures 2019, Hannover
• 2018: Elastoplastic Winkler models for suction caissons in clay. 9th NUMGE Conference, Porto
• 2018: Advanced foundation modelling: fast design methods for suction caisson foundations. REMS CDT Guest Lecture, Cranfield University

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:

• Carnegie Trust
• ETP Scotland

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.