Rock Joint Surfaces Measurement and Analysis of Aperture Distribution under Different Normal and Shear Loading Using GIS

Geometry of the rock joint is a governing factor for joint mechanical and hydraulic behavior. A new method of evaluating aperture distribution based on measurement of joint surfaces and three dimensional characteristics of each surface is developed. Artificial joint of granite surfaces are measured,processed, analyzed and three dimensional approaches are carried out for surface characterization. Parameters such as asperity's heights, slope angles, and aspects distribution at micro scale,local concentration of elements and their spatial localization at local scale are determined by Geographic Information System (GIS). Changes of aperture distribution at different normal stresses and various shear displacements are visualized and interpreted. Increasing normal load causes negative changes in aperture frequency distribution which indicates high joint matching. However, increasing shear displacement causes a rapid increase in the aperture and positive changes in the aperture frequency distribution which could be due to unmatching, surface anisotropy and spatial localization of contact points with proceeding shear

Modeling of Social Transitions Using Intelligent Systems

In this study, we reproduce two new hybrid intelligent systems, involve three prominent intelligent computing and approximate reasoning methods: Self Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory (RST),called SONFIS and SORST. We show how our algorithms can be construed as a linkage of government-society interactions, where government catches various states of behaviors: solid (absolute) or flexible. So, transition of society, by changing of connectivity parameters (noise) from order to disorder is inferred

Design and Development of Three-dimensional Laser Roughness Measurement Apparatus

Rock mass behavior is closely depends on rock joints characteristics, rock joint behavior is depends on its surfaces roughness. There are several methods to measure joint surfaces roughness by two or three dimensional. Three dimensional methods are expensive and advanced technology, thus to obtain high precision roughness of joint surfaces a new apparatus is designed and developed for first time in Iran. Using the designed apparatus the joint surfaces measurement with high precision is possible with low expenses. The apparatus is made up of two parts consisting hardware and software. Hardware consists of: laser head, cross table, and digital camera. The measured data is processed in software to obtain three dimensional surfaces roughness. As an advantageous of the developed apparatus it is possible to determine surface roughness using non-destructive method and same sample could be used for mechanical laboratory testing. To verify the apparatus performance a rock joint specimen roughness profiles is measured with 1mm intervals and the surface topography of joint surface is obtained with 1mm*1mm cell size. In this paper the apparatus assembly and its application along with the results are illustrated

Effect of induction motors mechanical load on its model for purpose of static voltage stability analysis

Load modelling is one of the major issues in voltage stability analysis. It greatly impacts the result of the study and has a significant effect on the stability regions. This paper examines the validity of static models for induction motors, proposed for static voltage stability studies, in their different operating conditions. It has been shown that motor operating condition and mechanical load, largely influences motor model. The effect has been fully studied in this paper and the results have been presented

Tunnel Probabilistic Structural Analysis Using the FORM

In this paper tunnel probabilistic structural analysis (TuPSA) was performed using the first order reliability method (FORM). In TuPSA, a tunnel performance function is defined according to the boundary between the structural stability and instability. Then the performance function is transformed from original space into the standard normal variable space to obtain the design point, reliability index, and also the probability of tunnel failure. In this method, it is possible to consider the design factors as the dependent or independent random parameters with arbitrary probability distributions. A software code is developed to perform the tunnel probabilistic structural analysis (TuPSA) using the FORM. For validation and verification of TuPSA, a typical tunnel example with random joints orientations as well as mechanical properties has been studied. The results of TuPSA were compared with those obtained from Monte-Carlo simulation. The results show, in spite of deterministic analysis which indicates that the rock blocks are stable, that TuPSA resulted in key-blocks failure with certain probabilities. Comparison between probabilistic and deterministic analyses results indicates that probabilistic results, including the design point and probability of failure, are more rational than deterministic factor of safety

Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach

Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium. This effect disrupts transmitted waves and prevents coherent summation of echo signals, resulting in suboptimal image quality. In real experiments, obtaining non-aberrated ground truths can be extremely challenging, if not infeasible. It hinders the performance of deep learning-based phase aberration correction techniques due to sole reliance on simulated data and the presence of domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require reference data to compensate for the phase aberration effect. We train a network wherein both input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training the network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Source code is available at \url{http://code.sonography.ai}

Laboratory and numerical investigation on strength performance of inclined pillars

Pillars play a critical role in an underground mine, as an inadequate pillar design could lead to pillar failure, which may result in catastrophic damage, while an over-designed pillar would lead to ore loss, causing economic loss. Pillar design is dictated by the inclination of the ore body. Depending on the orientation of the pillars, loading can be axial (compression) in horizontal pillars and oblique (compression as well as shear loading) in inclined pillars. Empirical and numerical approaches are the two most commonly used methods for pillar design. Current empirical approaches are mostly based on horizontal pillars, and the inclination of the pillars in the dataset is not taken into consideration. Laboratory and numerical studies were conducted with different width-to-height ratios and at different inclinations to understand the reduction in strength due to inclined loading and to observe the failure mechanisms. The specimens’ strength reduced consistently over all the width-to-height ratios at a given inclination. The strength reduction factors for gypsum were found to be 0.78 and 0.56, and for sandstone were 0.71 and 0.43 at 10? and 20? inclinations, respectively. The strength reduction factors from numerical models were found to be 0.94 for 10? inclination, 0.87 for 20? inclination, 0.78 for 30? inclination, and 0.67 for 40? inclination, and a fitting equation was proposed for the strength reduction factor with respect to inclination. The achieved results could be used at preliminary design stages and can be verified during real mining practice