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The existence of structuration in natural clays and shales is believed to change their stiffness, yielding, dilatancy and strength characteristics. These constitutive features are widely known to ultimately reunite with those of the reconstituted parent soil upon large straining. However, some experimental results show that such reunification may not occur in isotropic/one-dimensional compression, especially with regard to the critical state friction angle. This peculiar phenomenon has been barely addressed in constitutive models for natural geomaterials. Hence, the present study aims at introducing a structure-dependent critical state friction angle within the subloading yield framework. A new internal variable is introduced in the model of Nakai et al. (Soils Found 51(6):1149–1168, 2011) to capture subtle irreversible degradation of the structured critical state line which also serves as the threshold between contractive and dilatant volume changes. Additionally, a new evolution rule for the proposed destructuration factor is developed by considering important microstructural information revealed by discrete element method simulations. The proposed new modifications not only enhance the model capabilities in predicting bonding effects, but also enrich the classical stress-dilatancy equation by rendering it a function of void ratio, mean stress and the microstructural state. Model simulations of laboratory experimental tests on the Colorado shale as well as Bacinetto clay are presented in order to illustrate the improved predictive capabilities of the new model. 相似文献
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Mohamadi Sedigheh Sammen Saad Sh. Panahi Fatemeh Ehteram Mohammad Kisi Ozgur Mosavi Amir Ahmed Ali Najah El-Shafie Ahmed Al-Ansari Nadhir 《Natural Hazards》2020,104(1):537-579
Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer... 相似文献
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Analysis of geological structure and anthropological factors affecting arsenic distribution in the Lahore aquifer,Pakistan 总被引：1，自引：0，他引：1
Akhtar Malik Muhammad Tang Zhonghua Zakari Sissou Bahaa Mohamadi Muhsan Ehsan 《Hydrogeology Journal》2016,24(7):1891-1904
This study investigated the potential factors affecting arsenic concentration in the groundwater system of Lahore, Pakistan. The effects of several factors such as population density (PD), pumping rate (PR), impermeable land use (LU), surface elevation (SE), and water-table elevation (WL) on arsenic concentration were studied in 101 union councils of Lahore. Forty single and multi-factor models were established using geographic information system (GIS) techniques to develop an arsenic contamination map and to investigate the most effective combinations among factors. Additionally, statistical tests were used to evaluate arsenic concentration between classes of the same single factor. The arsenic concentration in the Lahore aquifer varied from 0.001 to 0.143 mg L?1. The highest arsenic concentrations were detected in the Walled City and the town of Shahdara. Among the 40 raster models, groundwater arsenic concentration showed the best matching frequency with single-factor models for PD (50.70 %) and SE (47 %). Thus, PD and SE were used to develop an arsenic distribution raster map, and they were also used to study the effect of aquifer depth on arsenic concentration. PD was found to have hidden latent variables such as PR and LU. The shallow aquifer depth was negatively correlated with arsenic concentration (r?=??0.23) and positively with PR (r?=?0.15). Therefore, when there was high PR in wells with smaller aquifer depth, the arsenic concentration was high. The existing water treatment and alternative water resources are good options, which should be developed to deal with Lahore wells contaminated with arsenic at high concentrations. 相似文献
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The present paper investigates the mechanical behaviour of oil sand specimens in triaxial compression tests at both ambient and elevated temperatures. The emphasis is particularly on core sample disturbance and on the multiphase/strongly heterogeneous nature of the material that introduces difficulties in achieving an objective characterization of its shear behaviour. First, the effect of sample disturbance on the behaviour of the oil sand is studied. Tests are performed on both disturbed and recompressed specimens. Recompression to large stress prior to shearing improves evaluation of the initial stiffness and associated volumetric changes of the oil sand, strongly affected by sample disturbance. A method for the correction of test results obtained from disturbed specimens is also proposed. The corrected results are in good agreement with those pertaining to recompressed specimens. Furthermore, a general classification of the tested oil sands into lean and rich in bitumen, where the former shows much softer and weaker behaviour, is considered to help in addressing the variability in sample composition. As for thermal aspects, the experimental results indicate that both strength and stiffness exhibit a limited temperature dependency. The temperature does not affect lean oil sand specimens, whereas heating considerably increases deformability of rich specimens. 相似文献
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Masoud Monjezi Hasan Ali Mohamadi Bahare Barati Manoj Khandelwal 《Arabian Journal of Geosciences》2014,7(2):505-511
In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height. 相似文献
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