Failure mode and strength anisotropic characteristic of stratified rock mass under uniaxial compressive situation LU Guang-yin(鲁光银), ZHU Zi-qiang(朱自强), LIU Qun-yi(柳群义), HE Xian-qi(何现启) Abstract: A stratified rock mass model was founded by FLAC 3D . The failure mode and anisotropic characteristic of strength for stratified rock mass were analyzed. The analysis results show that the numerical simulation can visually reflect the failure modes of rock samples under different inclination angles β of structural plane. The stiffness of rock sample before peak strength changes in the compressive procedure. With the increase of β, the compressive strength σ c of rock sample decreases firstly and then increases; when β is in the range of 20˚−30˚ and 80˚−90˚, σ c has the largest sensitivity to β; while β falls in the range of 30˚−70˚, σ c varies little. When φ j <β<90˚ (φ j is friction angle of structure plane), the results obtained from numerical simulation and theoretical analysis are in almost the same values; while β≤ φ j or β=90˚, they are in great different values. The results obtained from theoretical analysis are obvious larger than those from numerical simulation; and the results from numerical simulation can reflect the difference of compressive strength of rock samples for the two situations of β≥φ j and β=90˚, which is in more accordance with the real situation.
Abstract-Based on the nonlinear theory of cable-stayed bridge, nonlinear effects-cable sag, beam column effect, large displacement are calculated accurately using element geometric stiffness matrix, CR formulation, bar unit, Ernst formula and the catenary equation. Nonlinear analysis is applied on the study of cable force, the stress in main girder, pylon stress and tower deviation of the four towers cable-stayed bridge. The calculation results considering nonlinear effect is compared with which nonlinear effect is not considered under completion state. Results show that the geometric nonlinearity effect on the cable force is 0.09 ~ 1.04%; the effect on cumulative vertical displacement is -5 ~ 5mm; the effect on the lower edge stress of main girder is 0.5 ~ 1.5%; the effects on the higher edge stress of main girder is 0 ~ 0.5%. The effect on the lower edge stress of girder is significantly higher than the upper edge. The effect on the stress of tower is -0.1 ~ 0.1MPa; the effect on horizontal deviation difference at the top of tower is 1.4%~3.1%. Results show that the geometric nonlinearity has a certain effect on the state of bridge, the effects of geometric nonlinearity should be considered in design and construction control calculation.
Hydrodehalogenation or hydrosulfonate‐dehalogenation products, hydrohalofluorocarbons or 2‐hydropolyhalofluoroalkanesulfonates, were formed respectively from the title reaction under mild condition in good yield.
In urban environments, the complex and uncertain intersection scenarios are challenging for autonomous driving. To ensure safety, it is crucial to develop an adaptive decision making system that can handle the interaction with other vehicles. Manually designed model-based methods are reliable in common scenarios. But in uncertain environments, they are not reliable, so learning-based methods are proposed, especially reinforcement learning (RL) methods. However, current RL methods need retraining when the scenarios change. In other words, current RL methods cannot reuse accumulated knowledge. They forget learned knowledge when new scenarios are given. To solve this problem, we propose a hierarchical framework that can autonomously accumulate and reuse knowledge. The proposed method combines the idea of motion primitives (MPs) with hierarchical reinforcement learning (HRL). It decomposes complex problems into multiple basic subtasks to reduce the difficulty. The proposed method and other baseline methods are tested in a challenging intersection scenario based on the CARLA simulator. The intersection scenario contains three different subtasks that can reflect the complexity and uncertainty of real traffic flow. After offline learning and testing, the proposed method is proved to have the best performance among all methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.