Bayesian Optimization for CPT-Based Prediction of Impact Pile Drivability
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 149, Issue 11
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.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The driving and SI data are provided by a third party and may be available on request.
Acknowledgments
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. The provision of data and valuable input from Scottish Power Renewables is also gratefully acknowledged.
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© 2023 American Society of Civil Engineers.
History
Received: Oct 2, 2022
Accepted: Jun 26, 2023
Published online: Aug 31, 2023
Published in print: Nov 1, 2023
Discussion open until: Jan 31, 2024
ASCE Technical Topics:
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