Quasi-site-specific prediction of shear wave velocity from CPTu

Abstract

A rapid and reasonable estimation of the local site shear wave velocity (Vs) profile is indispensable for site assessment. For soil deposits, researchers have developed numerous empirical correlations between Vs and piezocone penetration test (CPTu) based on a global/regional (generic) database. However, these transformations are usually biased for local sites because the generic database contains all available data without regard to site-specific differences, and the transformation uncertainty is usually non-negligible due to the complicated geology factors and measurement errors. Moreover, it is challenging to construct a site-specific correlation due to the sparse in-situ measurements. To this end, this study develops a quasi-site-specific CPTu-Vs transformation model that algorithmically combines both the generic database and site-specific measurements to provide reliable Vs prediction with uncertainty description. A generic database containing 3116 cases of 6 parameters is collected from different countries, including corrected cone tip resistance, sleeve frictional resistance, depth, soil behavior index, pore pressure ratio, and Vs. As the significant impact of soil type on CPTu-Vs correlation, independent Johnson transformation (JS) for different soil types is first adopted to normalize the generic database as a whole, ensuring the constructed model is applicable to any soil type site. Three state-of-art quasi-site-specific models are investigated for CPTu-Vs transformation: probabilistic multiple regression (PMR), the hybridization method (HYD), and the hierarchical Bayesian model (HBM). Empirical correlations and the recently-developed data-driven model (XGBoost) are also considered to illustrate the novelty of the quasi-site-specific model. The applications on three sites with different soil types show that considering soil type in JS improves model accuracy and reduces the transformation uncertainty. The HBM with multiple basic CPTu parameters performs best regarding accuracy, uncertainty, and robustness.