To satisfy the economic requirements and reduce the impact to the surrounding buildings and underground structures, the dynamic compaction (heavy tamping) and static compaction are combined used in the soil filling for airport subgrade. Despite compaction the subgrades in the same degree of compaction, the subgrades filled by dynamic and static compaction method show different increase potential in the permanent strain under cyclic loading, which then further result in the differential settlement and safety problems. This study firstly investigated the compaction characteristics under static compaction and different dynamic compaction scheme, during which the static and dynamic compaction strain and stress evolutions were monitored. The cyclic triaxial tests were then performed to investigate the sample preparation method derived difference in permanent strain under cyclic loading. Furthermore, to provide a microscopic interpretation to this difference, the pore size distributions of the silt samples based on mercury intrusion porosimetry (MIP) test and the internal particle contact stresses from discrete element method (DEM) simulation were respectively explored. The main conclusions are as follows: (1) The dynamic compaction processes can be divided into rapid and slow compaction strain stages determined by strain growth rate and compaction numbers, which further influences the homogeneity of soil samples; (2) The statically compacted samples have more significant permanent strain than the dynamic ones due to the localized stress concentration and different pore microstructures; the permanent strain increases with dynamic compaction energy until a stable stage is reached. (3) The MIP results show that the dynamic compaction transforms the macropores into mesopores; the higher compaction energy enhances this transforming effect but results in a decrease in the overall homogeneity.
Particle breakage of granular materials under cyclic shearing is related to a variety of engineering problems in geotechnical and transportation engineering. However, there is limited understanding regarding the detailed evolution law of breakage under cyclic shearing. To this end, a series of cyclic simple shear tests were conducted on artificially dyed gypsum particles. It was observed that, in contrast to the particle size distribution of the whole sample, the fractional particle size distributions of gap-graded samples followed a unified breakage evolution path as those of the uniformly graded ones and tended towards fractional fractal distributions. These results have inspired the introduction of the fractional breakage index. Moreover, the breakage-plastic work relationship was further extended to describe the breakage of fractional particles, incorporating the effect of the number of cycles on the plastic work distribution. Finally, based on the concept of breakage-packing, a predictive model for plastic work-breakage-deformation of crushable granular materials under cyclic shearing was proposed. These results have the potential in understanding the detailed particle breakage evolution and establishing a predictive framework for the breakage-induced deformation of crushable granular materials.
The determination of S-wave velocity (Vs) is of significant importance in various engineering disciplines, including mining, civil, and geotechnical engineering. It is beneficial to indirectly determine Vs under both dry and saturated conditions and to understand its relationship with influencing input variables: coring depth (H), durability index (DI), water content (Wa), dry density (ρd), saturated density (ρs), and porosity (n). In this study, we evaluate these relationships using three multiple machine-learning algorithms (MLAs): artificial neural network (ANN), fuzzy inference system (FIS), and gene expression programming (GEP), alongside a linear regression method (LRM) and predict both dry S-wave velocity (Vs-dry) and saturated S-wave velocity (Vs-sat) of rocks. The research involves the analysis of 90 datasets derived from samples of schist, phyllite, and sandstone rocks collected from Azad and Bakhtiari dam sites in Iran. The diversity of these datasets is a key advantage of this study, providing a solid foundation for models training and testing while enhancing the models’ generalizability. Model optimization techniques are employed in the Python, MATLAB, GenXProTools, and SPSS environments to identify the most effective versions of ANN, FIS, GEP, and LRM models, respectively. The prediction performance analysis reveals that all applied models yield acceptable levels of accuracy for predicting Vs-dry and Vs-sat. However, GEP emerges as the best model for predicting both Vs-dry and Vs-sat. The ANN and FIS models also achieve high levels of accuracy, while LRM performs comparatively less well. Additionally, sensitivity analysis conducted using the cosine amplitude method (CAM) highlights the influence of different variables on Vs-dry and Vs-sat. The ρd is found to be the most influential parameter on Vs-dry, whereas DI exhibits the least impact. Conversely, the ρs significantly affects Vs-sat, while Wa shows the lowest impact. The exceptional performance of these proposed MLAs confirms their applicability in real-world rock engineering and geotechnics projects, offering precise determination of Vs. The diversity of studied rock types and datasets, along with the use of cost-effective and easy measurable inputs, the determination of Vs in both dry and saturated status, and the application of robust MLAs for Vs determination are the main novelties of this study. However, further researches involving additional datasets and more rock types are required to validate these findings.