In geotechnical site investigation, the distance between boreholes often makes the inter-borehole soil layer inferences rely on human experience under conditions of limited in situ tests and sampling. How to conduct intelligent and reliable soil stratigraphic division is a crucial research direction in the current information-oriented development of geotechnical engineering. This paper presents an intelligent soil stratigraphic layer division method by combining Bayesian Compressed Sensing (BCS), Support Vector Machine (SVM) classification, Gaussian Mixture Model (GMM), and Hidden Markov Random Field (HMRF) model. The application flowchart and soil layer division results are presented by taking the Nanjing Yangtze River floodplain ground as an example. The study shows that: BCS can reliably extend the blow count data from standard penetration test (SPT) for subsequent soil layer division; SVM classification can intelligently learn soil boundaries in the two-dimensional space of SPT blow count versus test depth, achieving an initial soil stratigraphic division; based on this, the preliminary optimization of soil layer division can be realized by using the GMM by considering the probability distribution of soil characteristic parameters; finally, the secondary optimization of soil layer division can be realized by using the HMRF model by incorporating spatial correlation constraints (i. e., adjacent points tending to be the same soil type). Combining the four methods can intelligently and automatically divide soil layers, and can significantly improve the accuracy of overall soil layer division and soil layer boundary identification.

The cumulative deformation and energy-dissipation behavior of soil under cyclic loading is an important basis for geotechnical engineering design. Undrained cyclic triaxial tests were carried out on retrieved and remolded silty clay samples. The effects of soil structure on the dynamic deformation and energy dissipation behavior were studied. The results show that: The dynamic deformation behavior of silty clay is controlled by cyclic stress ratio (CSR), and the threshold CSR for retrieved samples is higher than that of remolded samples. The development modes of double-amplitude strain with loading cycles can be divided into three types: stable, transitional, and failure modes. Similarly, the development of accumulated dissipated energy with double-amplitude strain can be categorized into three types. Under the same double-amplitude strain, the energy dissipation capability of remolded samples is higher that that of retrieved samples. Under the same test conditions, it takes more energy dissipation and loading cycle for retrieved samples to arrive at the same double-amplitude strain than for remolded samples. Soil structure has significant effects on the deformation and energy dissipation behavior of silty clay under cyclic loading.