Interpretation of cone penetration tests — a unified approach

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Predicting standard penetration test N-value from cone penetration test data using artificial neural networks (2017, methodology): “The Unified Soil Classification System (USCS) was used to classify the collected samples of the SPTs. Tip resistance (qc) and friction ratio (Rf) of the CPTs were used to classify the soil based on the soil behavior type (SBT) charts proposed by Lunne et al. (1997) and Robertson (2009).”
Bayesian identification of soil stratigraphy based on soil behaviour type index (2019, methodology): “(1), as shown in Figure 2(c), where the boundaries between different SBTs based on the Ic are shown by vertical dashed lines.”
Statistical characterization of random field parameters using frequentist and Bayesian approaches (2016, methodology): “of the normalized cone resistance Qtn (Robertson 2009) with n = 0.”
DEM modelling of cone penetration tests in a double-porosity crushable granular material (2016, methodology): “The most common approach (Lunne et al. 1997; Schnaid, 2009; Robertson, 2009) to 27 estimate strength, stiffness and other engineering properties of granular soils using CPT is to 28 first infer soil state variables (e.”