Fast CPT-based soil stratification using reversible jump Markov chain Monte Carlo simulation

Cone penetration test (CPT) is widely used in geotechnical site investigation. Several methods have been developed to automatically identify soil stratigraphy based on a single CPT sounding, which is an essential step in interpreting CPT data. Among existing methods, Bayesian methods allow probabilistic reasoning of soil stratigraphy from a given CPT sounding with explicit uncertainty quantification. However, Bayesian soil stratification methods based on CPT data tend to be computationally demanding or even prohibitive, particularly at sites with excessive soil strata due to the high dimensionality of soil stratification models. To address this issue, this study proposes a Bayesian method for fast soil stratification based on a single CPT sounding using a trans-dimensional sampling technique known as reversible jump Markov chain Monte Carlo (RJMCMC) simulation. The RJMCMC simulation is specifically tailored for Bayesian soil stratification by developing proposal distributions for three different Markov chain moves. The proposed method is illustrated and validated using benchmark examples and two real-life CPT soundings. It accomplishes CPT-based probabilistic soil stratification within 1–2 min. The computational time is generally unaffected by CPT sounding depth or the number of soil layers, making it feasible to stratify soil profiles with an excessive number of soil layers.