Defining the soil stratigraphy from seismic piezocone data: A clustering approach

The geotechnical ground investigation involves several testing techniques. Within such techniques, the Piezocone Penetration Test with seismic wave measurements (SCPTu) is one of the most popular. This test is widely used for in situ soil characterisation given its continuous measurements and repeatability. The interpretation of such test results has a strong theoretical background, namely allowing the identification the soil profile based on correlation of mechanical properties. In turn, the application of multivariate statistical methods allows identifying the soil stratigraphy by data association. This paper proposes a multivariate statistical approach, based on cluster analysis, for defining stratigraphic interfaces from SCPTu measurements. Using an extensive database from the Lower Tagus Valley region (near Lisbon, Portugal), this novel approach combines the four SCPTu parameters and recognises the association between the measurements. A comparison between statistical results against the profiles of soil behaviour index validates the efficiency of the approach, indicating a good agreement between data associations with the soil behaviour index profiles. The findings showed that the statistical procedure proposed in this study leads to reliable results for identifying soils with similar soil behaviour type.

Automated summary

The geotechnical ground investigation involves various testing techniques, with the Piezocone Penetration Test with seismic wave measurements (SCPTu) being a popular choice due to its continuous measurements and repeatability. Interpretation of the SCPTu test results involves a strong theoretical background and the application of multivariate statistical methods for identifying soil stratigraphy through data association. The proposed multivariate statistical approach, based on cluster analysis, shows promising results in defining stratigraphic interfaces from SCPTu measurements, with good agreement between data associations and soil behaviour index profiles.