Scripts underlying the publication: Predicting subsurface classification in 2D from cone penetration test data
DOI: 10.4121/e36a2326-5492-420e-9fe5-05299c10d28c
Dataset
Categories
Licence Apache-2.0
This dataset contains the script underlying the manuscript titled 'Predicting subsurface classification in 2D from cone penetration test data'. The script can be used for predicting the most likely 1D classification of the subsurface at CPT locations. The uncertainties in the CPT measurements and transformation to soil units are accounted for by integrating the Robertson chart with the Bayesian approach. The classification is carried out in two steps: (a) for a given number of layers, identifying the most probable soil layer thicknesses, and (b) identifying the most likely number of layers which, together with the most probable thicknesses, maximises the likelihood of observing the CPT measurements. The script can be used with any CPT data in .gef format.
History
- 2025-01-13 first online, published, posted
Publisher
4TU.ResearchDataFormat
Zipped python filesAssociated peer-reviewed publication
Predicting subsurface classification in 2D from cone penetration test dataDerived from
Funding
- SOFTTOP: Investigating heterogeneous soft top soils for wave propagation, cyclic degradation and liquefaction potential (grant code DEEP.NL.2018.006) [more info...] Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Organizations
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience & EngineeringDATA
Files (1)
- 14,380 bytesMD5:
1c7ce5e81026c2f246526a807f942f23
Probabilistic CPT-based soil classification.zip