Bayesian optimisation for CPT-based prediction of impact pile driveability

Buckley, R. , Chen, Y., Sheill, B., Suryasentana, S., Xu, D., Doherty, J. and Randolph, M. (2023) Bayesian optimisation for CPT-based prediction of impact pile driveability. Journal of Geotechnical and Geoenvironmental Engineering, 149(11), (doi: 10.1061/JGGEFK.GTENG-11385)

Abstract

Pile drivability predictions require information on the pile geometry, impact hammer, and the soil resistance to driving (SRD). Current SRD prediction methods are based on databases of long slender piles from the oil and gas industry and new, robust, and adaptable methods are required to predict SRD for current offshore pile geometries. This paper describes an optimization framework to update uncertain model parameters in existing axial static design methods to calibrate SRD. The approach is demonstrated using a case study from a German offshore wind site. The optimization process is undertaken using a robust Bayesian approach to dynamically update uncertain variables during driving to improve simulations. The existing method is shown to perform well for piles with geometries that reflect the underlying database such that only minimal optimization is required. For larger diameter piles, relative to the prior best estimate, optimized results are shown to provide significant improvements in the mean calculations and associated variance of pile drivability as more data is acquired. The optimized parameters can be used to predict SRD for similar piles in analogous ground conditions. The demonstrated framework is adaptable and can be used to develop site-specific calibrations and advance new SRD methods where large pile driving data sets are available.

• Item Type: Articles
• Additional Information: The iDrive (Intelligent Driveability Forecasting for Offshore Wind Turbine Monopile Foundations) project was supported by the Supergen Offshore Renewable Energy Hub flexible funding scheme. The ORE Hub is part of the wider Supergen Programme funded by the Engineering and Physical Sciences Research Council.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Buckley, Dr Roisin
• Authors: Buckley, R., Chen, Y., Sheill, B., Suryasentana, S., Xu, D., Doherty, J., and Randolph, M.
• College/School: College of Science and Engineering > School of Engineering > Infrastructure and Environment
• Journal Name: Journal of Geotechnical and Geoenvironmental Engineering
• Publisher: American Society of Civil Engineers
• ISSN: 1090-0241
• ISSN (Online): 1943-5606
• Published Online: 31 August 2023
• Copyright Holders: Copyright © 2023 American Society of Civil Engineers
• First Published: First published in Journal of Geotechnical and Geoenvironmental Engineering 149(11)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314446		iDrive: Intelligent Driveability Forecasting for Offshore Wind Turbine Monopile Foundations	Roisin Buckley	Engineering and Physical Sciences Research Council (EPSRC)	R74903	ENG - Infrastructure & Environment