Stephen Suryasentana

Postdoctoral Researcher
Engineering Science

College position:

The overarching goal of my research is to influence better civil engineering design (especially in the offshore renewables sector) through improved numerical modelling, soil constitutive modelling and 3D ground modelling. I develop robust and efficient numerical models of offshore wind foundations, which enables better optimisation of foundation designs and consequently, increased cost savings (which can be substantial for large-scale projects such as offshore wind farms). I am currently working on projects that use Bayesian machine learning methods to improve the reliability of offshore wind foundations, with a special emphasis on a) automated 3D ground modelling, b) increased efficiency of high-fidelity computational simulations, c) uncertainty estimates for identifying design risks, and d) automatic adaptation to better foundation designs with increasing data and experience.

Offshore renewable energy, Bayesian machine learning, Geotechnical engineering, Civil engineering, Constitutive modelling, Ground modelling, Foundation engineering, Soil-structure interaction