Probabilistic classification of soils based on Local Average Subdivision method and CPT data

Abstract

A random field generator based on Local Average Subdivision (LAS) method is proposed in this study in order to achieve probabilistic soil classification and quantify the uncertainty of the generated most probable geological cross section. CPT data and Robertson’s soil classification chart (1990) are adopted to classify the soil. The sole application of LAS makes the random field unconditional, which has been improved to conditional random field generator by using Kriging interpolation. Both unconditional and conditional generator are tested in an illustrative example and the results indicate that the improvement from unconditional to conditional random field reduces the uncertainty of the most probable result of classifications and the classifications in the unconditional random field will converge if there are enough realizations. Additionally, the conditional random field generator is further applied in a case with three conducted CPTs, which build up a domain with very large scale of fluctuations. It’s found that the uncertainty of the generated most probable result of classifications is pretty low so it’s speculated that the proposed generator can be best applied in a large scale of fluctuation scenario. Another finding in the case study is that the proposed random field generator can be used to verify the reliability of conducted CPTs.