Bayesian Optimization for CPT-Based Prediction of Impact Pile Drivability

Buckley, Róisín; Chen, Yuling Max; Sheil, Brian; Suryasentana, Stephen; Xu, Diarmid; Doherty, James; Randolph, Mark

doi:10.1061/jggefk.gteng-11385

Cited by 4 publications

(1 citation statement)

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“…Bayesian methods have gained significant attention in geotechnical engineering. They been employed to assess the probability of slope failure [23][24][25][26][27] and to predict tunnel Geotechnics 2024, 4 383 deformation [28][29][30][31][32], excavation movements [33][34][35][36][37], pipe-jacking forces [38], and pile driveability [39]. The advantages of Bayesian methods in geotechnical engineering include the ability to integrate multiple sources of information, such as expert opinions, field data, and laboratory tests, to reduce uncertainties and improve decision-making.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Bayesian Changepoint Detection Methods for Soil Layering Identification Using Cone Penetration Test Data

Suryasentana,

Sheil,

Lawler

2024

Geotechnics

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This paper assesses the effectiveness of different unsupervised Bayesian changepoint detection (BCPD) methods for identifying soil layers, using data from cone penetration tests (CPT). It compares four types of BCPD methods: a previously utilised offline univariate method for detecting clay layers through undrained shear strength data, a newly developed online univariate method, and an offline and an online multivariate method designed to simultaneously analyse multiple data series from CPT. The performance of these BCPD methods was tested using real CPT data from a study area with layers of sandy and clayey soil, and the results were verified against ground-truth data from adjacent borehole investigations. The findings suggest that some BCPD methods are more suitable than others in providing a robust, quick, and automated approach for the unsupervised detection of soil layering, which is critical for geotechnical engineering design.

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