Abstract
This paper showcases novel underground stratification based on sparse machine learning (SML) methods, including sparse Bayesian learning (SBL) and least absolute shrinkage selection operator (Lasso). The SML methods proposed by the authors were applied to two- and three-dimensional underground stratification of actual sites, Odagawa Riverbank (Okayama, Japan) and New Lock (Terneuzen, the Netherlands), to demonstrate their performances. Cone penetration test (CPT) data were available in both sites, and they were converted to the soil behavior type (SBT) index for the underground stratification analysis. The trends of Ic/SBT profiles and their distribution colormaps estimated by the two methods were compared to discuss the methodological characteristics. For the Odagawa Riverbank case, the detection ratio of SBT obtained by the two methods was also compared to investigate the estimation accuracy in terms of stratification ability.
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