Test on Mechanical Properties of Cemented Soils Using Secondary Development Data Acquisition Test System
A secondary development of computer aided data acquisition test system is performed in order to improve the disadvantages of original system. In this way, the qualified influences of cement content and curing time on yield strength and stiffness of soil can be automatic acquisition in consolidated undrained triaxial tests. The data collected from the system show that the deviatoric stress of soil increases with increasing axial strain initially followed by a relatively smooth decline after yield point. Yield strength increases with increasing cement content and curing time at the same strain. A well-defined linear relationship between yield strength and cement content are observed and also the relationship between yield strength and curing time. Finally, a linear function is defined to express the linear relationship between yield strength and those two factors and corresponding linear coefficients are obtained through a multiple regression analysis of test data. Stiffness of specimens shows an initial sharp degradation followed by smooth attenuation with increasing axial strain. The addition of cement and long curing time can effectively improve the stiffness of specimens especially at low strain areas.
Cavity expansion in cemented materials: structure degradation effects
A numerical solution of a cylindrical cavity expansion problem is presented herein, which is formulated within the framework of non-associated plasticity. The work extends the solution proposed by Mantaras & Schnaid in a companion paper in which a drained analysis has been developed based on postulates embedded on Rowe's stress dilatancy theory. This new formulation aims at demonstrating the significance of structure degradation during shear for a bonded cohesive-frictional material. The proposed solution makes it possible to derive the continuous variations of strains, stresses and volume changes produced around an expanding cavity. The significance of the method is demonstrated by a parametric study designed to highlight the influence of strength reduction during shear on cavity expansion analysis.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.