The long-lived debate on the principle of effective stress is rooted in the obscure physical significance of stresses. For the sakes of clarifying stress concepts and establishing a reasonable principle of effective stress, unsaturated soil is divided into six phases and the bearing structure of it, named generalized soil structure, is defined based on considering soil as a special structure. Then the essence of effective stress equation, named stress relation equation, is derived according to analysis of interphase interactions and independent-phase equilibrium. The stress relation equation indicates the corresponding relation between two series of stress variables used in mixed and multiphase continuum models, respectively. Furthermore, a reasonable concept of suction stress is redefined to describe interparticle connection properties. Then, a generalized stress framework is constructed by associating stress relation equation with suction stress. After demonstrating the concept of neutral stress, a generalized principle of effective stress is established and the total soil skeleton stress is searched out, which is the predominant stress controlling the strength and deformation of soil. Finally, the collapse phenomenon is analyzed and the time- and spatial-dependent stress frameworks are developed.
Acta Geotechnica - Many civil engineering projects are related to hydromechanical behavior of unsaturated soils over a wide suction range, which was investigated by imposing suctions on clayey silt... 相似文献
Acta Geotechnica - This article presents a new test prototype that leverages the 3D printing technique to create artificial particle assembles to provide auxiliary evidences that supports the... 相似文献
A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.
