Cone penetration data classification by Bayesian inversion with a Hidden Markov model

Abstract

This study examines the application of the Hidden Markov model (HMM) to the soil classification based on Cone Penetration Test (CPT) measurements. The HMM is formulated in the Bayesian framework and composed of a Markov chain prior and a Gaussian likelihood model. The application of the Bayesian framework is considered as suitable because it allows for the integration of different sources of information commonly available in a CPT-based soil classification. The occurrence of different soil classes along a CPT profile is modeled with the Markov chain, while the Gaussian likelihood model establishes a relation between the different soil classes and CPT measurements. Preliminary performance of the HMM is examined on the classification of CPT measurements from the Sheringham Shoal Offshore Wind Farm.