Interpretation of Cone Penetration Tests to Characterize Tropical Residual Soils Using Machine Learning

Abstract

Cone penetration test (CPT) has been strongly applied to identify the soil profile and to provide some estimation of soil parameters. Several correlations exist, allowing the geo-characterization of the soil from CPT data. Such correlations must be carefully applied, and whenever possible, corrected with direct measurements of laboratory tests. Tropical residual soils have an inherent variability capable of providing very distinct results from very similar samples. Project designers must deal with this variability and correctly characterize these materials. The present work focuses on a case study where the goal was to distinguish and characterize two soft soils existent on the foundation of a tailings dam in the southwest of Brazil. The construction of the dam is still ongoing, and its foundation belongs to a complex geological environment with soft soils that can reach NSPT blows as low as its own weight. The geological survey identifies two horizons of residual soil of dolomitic phyllite: soft and very soft. However, distinguishing spatially this material regarding its consistence has shown to be a challenging task. Since they differ essentially on the degree of weathering, most parameters for both materials are quite similar, and from laboratory tests, the parameter that helps differentiate these soils is the pore pressure Skempton parameter at failure – Af. In addition, the groundwater level in the area is not clear, complicating the estimation of the vertical effective stress profile and further parameters from the CPT analysis. To overcome this issue, a sensitive analysis of the influence of groundwater level on the parameters of interest in this work (apparent overconsolidation ratio) was performed. To get as much information as possible from all datasets available, an Exploratory Data Analysis (EDA) followed by the application of an unsupervised learning algorithm was performed. Although an exactly spatial division from these soils were not possible, the EDA and unsupervised learning allow better visualization of the spatial distribution of these soils and grouping by desired characteristics, such as the pore pressure parameter.