Efficient Simulation of 2D Non-Stationary CPT Profiles from an Incomplete Data Set Using Bayesian Compressive Sensing Methods

Cone penetration test (CPT) is one of the most widely used in situ methods for characterizing the spatial variability of site characterization, due to its rapidity, affordability, and repeatability. It is often encountered in practice that some CPTs probe deeper than others, and that some CPT soundings may contain missing data due to presence of gravel-sized particles or intentional bypassing of gravelly soil layers. Furthermore, the number of CPT soundings in a cross-section is often sparse, although the number of data points along the depth is adequate. In these cases, it is difficult and challenging to estimate spatially varying soil property at locations without CPT soundings, especially for non-stationary CPT within multi-layers. While certain methods have been proposed hoping to address these concerns, they are frequently constrained by either stationary assumption of data, autocorrelation function forms, or computational issues. This study presents a Bayesian compressive sensing-based machine learning methods for this issue, with both numerical and real-world CPT data for validation and demonstration. Results show that the proposed method performs reasonably well in simulating 2D non-stationary CPT profiles from incomplete data set.