Applicability of existing empirical shear wave velocity correlations to seismic cone penetration test data in Christchurch New Zealand

Seismic piezocone (SCPTu) data compiled from 86 sites in the greater Christchurch, New Zealand area are used to evaluate several existing empirical correlations for predicting shear wave velocity from cone penetration test (CPT) data. It is shown that all the considered prediction models are biased towards overestimation of the shear wave velocity of the Christchurch soil deposits, demonstrating the need for a Christchurch-specific shear wave velocity prediction model (McGann et al., 2014) [1]. It is hypothesized that the unique depositional environment of the considered soils and the potential loss of soil ageing effects brought about by the 2010–2011 Canterbury earthquake sequence are the primary source of the observed prediction bias.     

A back analysis scheme for refined soil stratification based on integrating borehole and CPT data

Utilizing both borehole and Cone Penetration Testing (CPT) data in soil stratification helps to get more convincing soil stratification results. However, the soil classification results revealed by borehole (Unified Soil Classification System, USCS) and CPT tests (soil behavior type, SBT) are commonly not consistent. This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method. The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types. A novel boundary dictionary method is proposed to enhance the model performance on complex soil layer conditions. A probabilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data. The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees. The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization (SMBO) algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix. The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method. The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method. The results indicate that the proposed method is robust and effective.