The fundamental understanding of the behavior of granular materials by the effect of vibration is necessary to properly address a number of engineering issues, such as long-term settlement of high-speed railway, vibratory pile driving in sandy stratum, and earthquake-induced geotechnical disaster. Triaxial compression tests of dry Pingtan sand were carried out by a modified triaxial apparatus, where axial high-frequency vibration was super-imposed on the specimen at pre-peak, peak, and post-peak stress states during monotonic shearing. The influences of vibration conditions, confining pressure, and the initial relative density on the vibration-induced responses of Pingtan sand are mainly considered. It is shown that the super-imposed vibration leads to significant deviatoric stress reduction and vibro-induced additional axial strain. This owes to the fact that the static inter-particle friction turns to dynamic friction, and consequently, the frictional resistance has a considerable reduction when vibration is applied to the sand specimen. The vibration-induced stress–strain behavior of sand specimen is characterized into three states by two thresholds concerning vibration intensity and confining pressure: (1) stable state, (2) vibro-compression state and (3) vibro-instability state. For the vibro-compression state, the deviatoric stress reduction has a positive linear correlation with the increase in vibration intensity, while the vibro-induced additional axial strain follows a power-law increase with vibration intensity. Given a vibration condition, the deviatoric stress reductions and the vibro-induced additional axial strains at pre-peak, peak, and post-peak stress state follow a descending order. Besides, the influences of vibration on shear strength and critical state were also discussed.
<正>1 Introduction Permeability of Member-6,Member-7,and Member-8,Triassic Yangchang Formation in Ordos Basin is lower than 1?0~(-3)μm~2,so sandstone in those formations are typical tight reservoir(Zhao et al.,2012a,2012b;Yang et al.,2013).Because of the maximum flooding event of Late Triassic during deposition of Chang-7 Member,the lacustrine basin had a wide range of deposition area and 相似文献
Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency. However, these pickers are trained with and applied to different data. A comprehensive benchmark based on a single dataset is therefore lacking. Here, using the recently released DiTing dataset, we analyzed performances of seven phase pickers with different network structures, the efficiencies are also evaluated using both CPU and GPU devices. Evaluations based on F1-scores reveal that the recurrent neural network (RNN) and EQTransformer exhibit the best performance, likely owing to their large receptive fields. Similar performances are observed among PhaseNet (UNet), UNet++, and the lightweight phase picking network (LPPN). However, the LPPN models are the most efficient. The RNN and EQTransformer have similar speeds, which are slower than those of the LPPN and PhaseNet. UNet++ requires the most computational effort among the pickers. As all of the pickers perform well after being trained with a large-scale dataset, users may choose the one suitable for their applications. For beginners, we provide a tutorial on training and validating the pickers using the DiTing dataset. We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users. All of our models are open-source and publicly accessible. 相似文献
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