Spatial interpolation of sparse PCPT data to optimise infrastructure design

In an offshore setting the geotechnical data available to infrastructure designers is usually sparse, and judgement is required in using information from sampled locations to estimate design parameters at unsampled locations. Recent interest in data-centric methods has seen advances in the interpolation of sparse data via statistical and analytical approaches. This paper demonstrates the implementation of one such approach, applying Bayesian Compressive Sensing and Markov Chain Monte Carlo techniques to sparse two-dimensional PCPT data. Through a simplified case study, the paper highlights how the method incorporates estimation uncertainty and its associated impact on the geotechnical design of a representative foundation.