GAN inversion method of an initial in situ stress field based on the lateral stress coefficient

Mechanical Field Guiding Structure Design Strategy for Meta-Fiber Reinforced Hydrogel Composites by Deep Learning

Fiber-reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta-fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta-fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta-fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta-fiber distribution. The well-trained GAN is implemented to design meta-fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics.

Behaviour of Horseshoe-Shaped Tunnel Subjected to Different In Situ Stress Fields

At great depths, tunnel openings experience a tectonic stress field rather than overburden stress. This paper aims to examine the impact of different in situ stress ratios and multiple tunnel depths below the surface on the excavation induced-stresses and displacements around tunnel openings. Thus, a series of models has been built, using a two-dimensional elasto-plastic finite-elements code, RS2D, to conduct parametric stability analysis. The performance of tunnel opening is examined by evaluating the induced stress-deformation around the opening. The results indicate that ratio of wall convergence, roof sag and floor heave increase as in situ stress ratio and tunnel depth below surface increase. Additionally, the induced-stresses increase as depth and state of in situ stress increase. In addition, the extent of yielding zones into rock mass around tunnel roof and floor deteriorates as tunnel depth and in situ stress ratio increase. Moreover, the normal stress along rock joints is sharply dropped when joints pass in the vicinity of tunnel opening (e.g., centre of opening). As well, the direction of shear stress along joints is reversed. Consequently, inward shear displacement of rock, on the underside of the weakness plane, is produced as a result of slip occurrence.