A semi-supervised clustering-based approach for stratification identification using borehole and cone penetration test data

Borehole drilling and cone penetration test (CPT) are frequently employed site investigation methods for identifying subsurface stratification. However, these two methods have their respective pros and cons, and their corresponding soil type classification protocols are different. Therefore, an approach that can jointly interpret raw data from both investigation methods and provide unified soil classification results is in great demand. Motivated by the aforementioned point, this paper presents a novel semi-supervised clustering based stratification identification approach using information from both boreholes and CPT logs. The proposed approach is established on a hidden Markov random field (HMRF) framework so that the supervision constraints could be introduced by using borehole data during the clustering of CPT sounding samples. Further, the presented approach employs a Monte Carlo Expectation Maximization (MCEM) algorithm to perform the clustering process, which enables estimating the subsurface stratification in a probabilistic manner. The performances of the proposed approach are evaluated using real-world site investigation data. The test results indicate that the proposed approach is effective and robust for identifying subsurface stratification.