Comparison of machine learning models in a data-driven approach for scalable and adaptive design of laterally-loaded monopile foundations

S. K. Suryasentana, H. J. Burd, B. W. Byrne, A. Aghakouchak, T. Sørensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

The design of monopile foundations under lateral loading is typically conducted using the Winkler modelling approach. Design models incorporating this approach include the 'p-y' method specified in the design codes or the more recent 'PISA' design model. These design models have predefined soil reactions that are appropriate only for the pile and soil characteristics considered in the calibration. One of the biggest uncertainties is thus defining the appropriate soil reactions for design soils with different characteristics from the calibration cases. This paper proposes a data-driven approach, in which machine learning models are used to determine the most appropriate soil reactions directly from data. As these models can automatically adapt and increase in complexity to new data, it provides for a more scalable approach than existing predefined design models. Three machine learning models (polynomial regression, neural network regression (NN) and Gaussian Progress (GP) regression) are compared. The results demonstrate that the polynomial regression model is the simplest to interpret but is the least accurate. The NN regression model has high accuracy, but is the hardest to interpret. The GP regression model is the most accurate and is reasonably easy to interpret. Moreover, the GP regression model has the additional benefit of providing intrinsic uncertainty estimates, making it appropriate for quantifying and reducing design risks.
Original languageEnglish
Title of host publicationInternational Symposium on Frontiers in Offshore Geotechnics
Place of Publication[Hawthorne, NJ.]
Publication statusPublished - 11 Aug 2020
EventInternational Symposium on Frontiers in Offshore Geotechnics - University of Texas, Austin, Austin, United States
Duration: 11 Oct 2020 → …

Conference

ConferenceInternational Symposium on Frontiers in Offshore Geotechnics
Abbreviated titleISFOG
Country/TerritoryUnited States
CityAustin
Period11/10/20 → …

Keywords

  • PISA
  • winkler
  • laterally loaded monopile
  • data driven
  • machine learning
  • neural network
  • Gaussian process regression

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