Shear Creep Simulation of Structural Plane of Rock Mass Based on Discontinuous Deformation Analysis

Numerical simulations of the creep characteristics of the structural plane of rock mass are very useful. However, most existing simulation methods are based on continuum mechanics and hence are unsuitable in the case of large displacements and deformations. The discontinuous deformation analysis method proposed by Genhua is a discrete one and has a significant advantage when simulating the contacting problem of blocks. In this study, we combined the viscoelastic rheological model of Burgers with the discontinuous deformation analysis (DDA) method. We also derived the recurrence formula for the creep deformation increment with the time step during numerical simulations. Based on the minimum potential energy principle, the general equilibrium equation was derived, and the shear creep deformation in the structural plane was considered. A numerical program was also developed and its effectiveness was confirmed based on the curves obtained by the creep test of the structural plane of a rock mass under different stress levels. Finally, the program was used to analyze the mechanism responsible for the creep features of the structural plane in the case of the toppling deformation of the rock slope. The results showed that the extended DDA method is an effective one

Lookaround Optimizer: kk steps around, 1 step average

Weight Average (WA) is an active research topic due to its simplicity in ensembling deep networks and the effectiveness in promoting generalization. Existing weight average approaches, however, are often carried out along only one training trajectory in a post-hoc manner (i.e., the weights are averaged after the entire training process is finished), which significantly degrades the diversity between networks and thus impairs the effectiveness in ensembling. In this paper, inspired by weight average, we propose Lookaround, a straightforward yet effective SGD-based optimizer leading to flatter minima with better generalization. Specifically, Lookaround iterates two steps during the whole training period: the around step and the average step. In each iteration, 1) the around step starts from a common point and trains multiple networks simultaneously, each on transformed data by a different data augmentation, and 2) the average step averages these trained networks to get the averaged network, which serves as the starting point for the next iteration. The around step improves the functionality diversity while the average step guarantees the weight locality of these networks during the whole training, which is essential for WA to work. We theoretically explain the superiority of Lookaround by convergence analysis, and make extensive experiments to evaluate Lookaround on popular benchmarks including CIFAR and ImageNet with both CNNs and ViTs, demonstrating clear superiority over state-of-the-arts. Our code is available at https://github.com/Ardcy/Lookaround.Comment: 18 pages, 9 figure