The activation energies of the grain-boundary and different polarizations are researched to reveal the effect of the segregation structure on the performance.

In order to determine the slip plane of slope directly by the calculation results of strength reduction method, and analyze the influential factors of slope stability, a numerical model was established in plane strain mode by FLAC3D for homogeneous soil slope, whose parameters were reduced until the slope reached the critical state. Then FISH program was used to get the location data of slip plane from displacement contour lines. Furthermore, the method to determine multiple slip planes was also proposed by setting different heights of elastic areas. The influential factors for the stability were analyzed, including cohesion, internal friction angle, and tensile strength. The calculation results show that with the increase of cohesion, failure mode of slope changes from shallow slipping to the deep slipping, while inclination of slip plane becomes slower and slipping volume becomes larger; with the increase of friction angle, failure mode of slope changes from deep slipping to shallow slipping, while slip plane becomes steeper and upper border of slip plane comes closer to the vertex of slope; the safety factor increases little and slip plane goes far away from vertex of slope with the increase of tensile strength.

In order to keep track of the operational state of power grid, the world's largest sensor systems, smart grid, was built by deploying hundreds of millions of smart meters. Such system makes it possible to discover and make quick response to any hidden threat to the entire power grid. Non-technical losses (NTLs) have always been a major concern for its consequent security risks as well as immeasurable revenue loss. However, various causes of NTL may have different characteristics reflected in the data. Accurately capturing these anomalies faced with such large scale of collected data records is rather tricky as a result. In this paper, we proposed a new methodology of detecting abnormal electricity consumptions. We did a transformation of the collected time-series data which turns it into an image representation that could well reflect users' relatively long term consumption behaviors. Inspired by the excellent neural network architecture used for objective detection in computer vision domain, we designed our deep learning model that takes the transformed images as input and yields joint featured inferred from the multiple aspects the input provides. Considering the limited labeled samples, especially the abnormal ones, we used our model in a semi-supervised fashion that is brought out in recent years. The model is tested on samples which are verified by on-field inspections and our method showed significant improvement.

The majority of jointed rock mass failures mainly occur along the joints in shear mode, which promotes a wide investigation on the proposal of a reasonable and reliable shear constitutive model of rock joints. In this paper, based on Improved Harris function and laboratory shear tests, a new constitutive model of saw-tooth joints was proposed. Firstly, a series of laboratory direct shear tests were carried out on saw-tooth joint specimens made of rock-like materials (cement mortar) to obtain the shear stress-displacement curves. Subsequently, the test results were divided into sliding failure type and peak shear type according to whether there is a significant stress drop between peak stress and residual stress. It is assumed that rock elements can be divided into undamaged parts and damaged parts during the shearing process. The stress-displacement relation of the undamaged part satisfies Hooke's law, while the damaged part provides residual stress. Via the comparison with commonly used micro-element failure probability density functions, the Improved Harris distribution function was selected as the standard to characterize the strength of micro rock units. Finally, derived from the theory of damage statistical mechanics, a damage statistical constitutive model was proposed, which can reflect the deformation characteristics of rock joints. Compared with previous models and experimental data, the model proposed in this paper can represent the trend of peak shear curve variation with higher accuracy, the parameters are easy to be solved and have obvious physical significance, which verifies the advantages and applicability of this model.

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