A bounding surface model is formulated to simulate the behavior of clays that are subject to an anisotropic consolidation stress history. Conventional rotational hardening is revisited from the perspective of thermodynamics. As the free energy cannot be accumulated infinitely upon critical state failure, the deviatoric back stress must vanish. This requires the rotated yield surface to be turned back to eventually align on the hydrostatic axis in the stress plane. Noting that most of the previous propositions violate this restriction, an innovative rotational hardening rule is formulated that is thermodynamically admissible. The bounding surface framework that employs the modified yield surface is applied to simulate elastoplastic deformations for overconsolidated clays, with which the overprediction of strength on the “dry” side can be greatly improved with reasonable results. Other important features, including contractive or dilative response and hardening or softening behavior, can also be well-captured. It has been shown that the model can simulate three types of reconstituted clays that are sheared with initial conditions over a wide range of anisotropic consolidation stress ratios and overconsolidation ratios under both triaxial undrained and drained conditions. Limitations and potential improvement of the model regarding the fabric anisotropy at critical state have been discussed. 相似文献
Extracting geochemical anomalies from geochemical exploration data is one of the most important activities in mineral exploration. Geochemical anomaly detection can be regarded as a binary classification problem. The similarity between geochemical samples can be measured by their distance. The key issue of this classification is to find the intrinsic relationship and distance between geochemical samples to separate geochemical anomalies from background. In this paper, a hybrid method that integrates random forest and metric learning (RFML) is used to identify geochemical anomalies related to Fe-polymetallic mineralization in Southwest Fujian Province of China. RFML does not require any specific statistical assumption on geochemical data, nor does it depend on sufficient known mineral occurrences as the prior knowledge. The geochemical anomaly map obtained by the RFML method showed that the known Fe deposits and the generated geochemical anomaly area have strong spatial association. Meanwhile, the receiver operating characteristic curves for the results of RFML and another method, namely maximum margin metric learning, indicated that the RFML method exhibited better performance, suggesting that RFML can be effectively applied to recognize geochemical anomalies.
