CPT Core

CPT Core offers a comprehensive solution for parsing and visualizing Cone Penetration Test (CPT) and Borehole Research-Geotechnics (BHR-GT) data. Allowing you to choose between seven automated soil classification models, including the CEMS machine learning model, Robertson, Been & Jefferies, and NEN, among others, CPT Core emerges as the premier tool for analyzing geotechnical profiles and initiating geotechnical computations.

One standout feature of CPT Core is its ability to mimic the decision-making process of a geotechnical engineer. It achieves this by segmenting layers based on cone resistance and local friction. With steering parameters at your disposal, you have the flexibility to define the number of layers according to your preference. This segmentation approach employs a weighted average of cone resistance and local friction to classify each layer, effectively filtering out noise present in the measurement signal at transition depths.

Furthermore, CPT Core provides soil parameters for each layer following the guidelines outlined in table 2.b of NEN9997-1. Each classification method adheres to predefined definitions for soil parameters, yet users retain the autonomy to override these settings, granting complete control over their soil profiles.

In summary, CPT Core not only streamlines the analysis of geotechnical data but also empowers users with customizable features and precise soil parameter estimations, making it an indispensable tool for geotechnical engineers and researchers alike.

API documentation

Also check our related blogs in the Stories:
The CPT MODEL (part 1)
Applications of the CPT MODEL (part 2)

Gef-Model

soil classification based on machine learning

NEN-Table

defines soil properties according to NEN9997-1 table 2b

PyGef

reads GEF-files and interpret cpts with Robertson / Been & Jefferies + plot

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