An analytical model for predicting the shear strength of unsaturated soils

2022 ◽  
pp. 1-57
Author(s):  
Tuan A. Pham ◽  
Melis Sutman
Keyword(s):  
Shear Strength ◽  
Analytical Model ◽  
Unsaturated Soils ◽  
High Performance ◽  
Micromechanical Model ◽  
Experimental Studies ◽  
Stress Equilibrium ◽  
Proposed Model ◽  
New Equation ◽  
Multi Phase

The prediction of shear strength for unsaturated soils remains to be a significant challenge due to their complex multi-phase nature. In this paper, a review of prior experimental studies is firstly carried out to present important pieces of evidence, limitations, and some design considerations. Next, an overview of the existing shear strength equations is summarized with a brief discussion. Then, a micromechanical model with stress equilibrium conditions and multi-phase interaction considerations is presented to provide a new equation for predicting the shear strength of unsaturated soils. The validity of the proposed model is examined for several published shear strength data of different soil types. It is observed that the shear strength predicted by the analytical model is in good agreement with the experimental data, and get high performance compared to the existing models. The evaluation of the outcomes with two criteria, using average relative error and the normalized sum of squared error, proved the effectiveness and validity of the proposed equation. Using the proposed equation, the nonlinear relationship between shear strength, saturation degree, volumetric water content, and matric suction are observed.

Model for the prediction of shear strength with respect to soil suction

1996 ◽  
Vol 33 (3) ◽  
pp. 379-392 ◽  
Author(s):  
S K Vanapalli ◽  
D G Fredlund ◽  
D E Pufahl ◽  
A W Clifton
Keyword(s):  
Shear Strength ◽  
Soil Water ◽  
Unsaturated Soil ◽  
Unsaturated Soils ◽  
Characteristic Curve ◽  
Experimental Studies ◽  
Matric Suction ◽  
Glacial Till ◽  
Soil Suction ◽  
Soil Water Characteristic Curve

Experimental studies on unsaturated soils are generally costly, time-consuming, and difficult to conduct. Shear strength data from the research literature suggests that there is a nonlinear increase in strength as the soil desaturates as a result of an increase in matric suction. Since the shear strength of an unsaturated soil is strongly related to the amount of water in the voids of the soil, and therefore to matric suction, it is postulated that the shear strength of an unsaturated soil should also bear a relationship to the soil-water characteristic curve. This paper describes the relationship between the soil-water characteristic curve and the shear strength of an unsaturated soil with respect to matric suction. Am empirical, analytical model is developed to predict the shear strength in terms of soil suction. The formulation makes use of the soil-water characteristic curve and the saturated shear strength parameters. The results of the model developed for predicting the shear strength are compared with experimental results for a glacial till. The shear strength of statically compacted glacial till specimens was measured using a modified direct shear apparatus. Specimens were prepared at three different water contents and densities (i.e., corresponding to dry of optimum, and wet of optimum conditions). Various net normal stresses and matric suctions were applied to the specimens. There is a good correlation between the predicted and measured values of shear strength for the unsaturated soil. Key words: soil-water characteristic curve, shear strength, unsaturated soil, soil suction, matric suction.

scholarly journals METALS HEAT TREATMENT MODEL

2020 ◽  
pp. 43-52
Author(s):  
Valery Ivaschenko ◽  
Gennady Shvachych ◽  
Maryna Sazonova ◽  
Olena Zaporozhchenko ◽  
Volodymyr Khristyan
Keyword(s):  
Heat Treatment ◽  
High Performance ◽  
Cold Deformation ◽  
Experimental Studies ◽  
Carbon Steels ◽  
Cold Forging ◽  
High Dispersion ◽  
Entire Plane ◽  
Temperature Conditions ◽  
Proposed Model

This paper considers the problem of developing a model of thermal metal processing by multiprocessor computing systems. The obtained metal is used for high-strength fasteners manufactured by cold forging method without final heat treatment. The model is based on the heat treatment method of a billet from low- and medium-carbon steels intended for cold heading. The model aims at improving technological properties of a billet by ensuring high dispersion and uniformity of a billet structure across the entire plane of its cross-section.Implementation of the proposed model ensures the technical result of high dispersion and uniformity of the structure of the billet. The technological process of steel heat treatment is characterized by high performance, low power consumption, and improved performance characteristics. The apparatus for implementation of the spheroidization annealing regime determines the uniform distribution of cementite globules in the ferrite matrix, which means that it provides the necessary mechanical properties of the metal for its further cold deformation. The multiprocessor computing system software allows controlling the temperature conditions, both on the entire plane of the billet section, and across its length. Such temperature conditions are controlled in the center of the plane of the billet cross-section.Experimental studies of the heat treatment of metal products were conducted. In order to test the functions of the proposed model, several experiments were performed when a 20 mm diameter wire from 20G2G steel was subjected to heat treatment. Experimental studies have shown that metal has the necessary elasticity properties, saving the required hardness.

A piecewise analytical model of bearingless switched reluctance motor for full rotor angular positions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi Wang ◽  
Honghua Wang ◽  
Jingwei Zhang ◽  
Chao Tan
Keyword(s):  
Analytical Model ◽  
Air Gap ◽  
Maxwell Stress ◽  
Calculation Methods ◽  
Content Type ◽  
Switched Reluctance ◽  
Excitation Mode ◽  
Proposed Model ◽  
Reluctance Motor ◽  
Multi Phase

Purpose This paper aims to establish a piecewise Maxwell stress analytical model of bearingless switched reluctance motor (BSRM) for the full rotor angular positions. The proposed model varies from the existing models, which are only applicable to the partial-overlapping positions of stator and rotor poles. By extending the applicable rotor angular positions, this model provides a basic analytical model for the multi-phase excitation control of BSRM. Design/methodology/approach The full rotor angular positions are classified into the partial-overlapping positions and the non-overlapping positions. At first, two different air gap subdividing methods are proposed, respectively, for the two-position ranges. Then, different integration paths are selected accordingly. Furthermore, two approximate methods are presented to calculate the average flux density of each air gap subdivision. Finally, considering the mutual coupling between the two perpendicular radial suspension forces, a piecewise Maxwell stress analytical model is derived for the full rotor angular positions of BSRM. Findings A piecewise Maxwell stress analytical model of BSRM is built for the full rotor angular positions, and applicable to the multi-phase excitation mode of BSRM. For the partial-overlapping positions and the non-overlapping positions, two sets of air gap subdividing methods, integration paths and approximate calculation methods of air gap flux densities are proposed, respectively. The accuracy and reliability of the proposed model are verified by the finite element method. Originality/value The piecewise Maxwell stress analytical model of BSRM for the full rotor angular positions is proposed for the first time. The novel air gap subdividing methods, integration paths, approximate calculation methods of air gap flux densities and the coupling between the two radial suspension forces are adopted to improve the modeling accuracy. As the applicable range of rotor angular position is extended, this model overcomes the limitation of the existing models only for single-phase excitation mode and contributes to the accurate control of BSRM multi-phase excitation mode.

A Relativistic Newtonian Mechanics Predicts with Precision the Results of Recent Neutrino-Velocity Experiments

2014 ◽  
Vol 6 (1) ◽  
pp. 1032-1035 ◽  
Author(s):  
Ramzi Suleiman
Keyword(s):  
Null Hypothesis ◽  
Experimental Studies ◽  
Theoretical Models ◽  
Newtonian Mechanics ◽  
Speed Of Light ◽  
Symmetry Principle ◽  
Proposed Model ◽  
Significant Difference ◽  
The One ◽  
Complete Collapse

The research on quasi-luminal neutrinos has sparked several experimental studies for testing the "speed of light limit" hypothesis. Until today, the overall evidence favors the "null" hypothesis, stating that there is no significant difference between the observed velocities of light and neutrinos. Despite numerous theoretical models proposed to explain the neutrinos behavior, no attempt has been undertaken to predict the experimentally produced results. This paper presents a simple novel extension of Newton's mechanics to the domain of relativistic velocities. For a typical neutrino-velocity experiment, the proposed model is utilized to derive a general expression for . Comparison of the model's prediction with results of six neutrino-velocity experiments, conducted by five collaborations, reveals that the model predicts all the reported results with striking accuracy. Because in the proposed model, the direction of the neutrino flight matters, the model's impressive success in accounting for all the tested data, indicates a complete collapse of the Lorentz symmetry principle in situation involving quasi-luminal particles, moving in two opposite directions. This conclusion is support by previous findings, showing that an identical Sagnac effect to the one documented for radial motion, occurs also in linear motion.

scholarly journals Estimating the phase volume fraction of multi-phase steel via unsupervised deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Wook Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
High Performance ◽  
Materials Science ◽  
Volume Fraction ◽  
Training Data ◽  
Phase Fraction ◽  
Phase Volume ◽  
Generative Adversarial Network ◽  
Phase Volume Fraction ◽  
Original Dataset ◽  
Multi Phase

AbstractAdvanced high strength steel (AHSS) is a steel of multi-phase microstructure that is processed under several conditions to meet the current high-performance requirements from the industry. Deep neural network (DNN) has emerged as a promising tool in materials science for the task of estimating the phase volume fraction of these steels. Despite its advantages, one of its major drawbacks is its requirement of a sufficient amount of training data with correct labels to the network. This often comes as a challenge in many areas where obtaining data and labeling it is extremely labor-intensive. To overcome this challenge, an unsupervised way of learning DNN, which does not require any manual labeling, is proposed. Information maximizing generative adversarial network (InfoGAN) is used to learn the underlying probability distribution of each phase and generate realistic sample points with class labels. Then, the generated data is used for training an MLP classifier, which in turn predicts the labels for the original dataset. The result shows a mean relative error of 4.53% at most, while it can be as low as 0.73%, which implies the estimated phase fraction closely matches the true phase fraction. This presents the high feasibility of using the proposed methodology for fast and precise estimation of phase volume fraction in both industry and academia.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

scholarly journals Recent Advances in ZnO-Based Carbon Monoxide Sensors: Role of Doping

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4425
Author(s):  
Ana María Pineda-Reyes ◽  
María R. Herrera-Rivera ◽  
Hugo Rojas-Chávez ◽  
Heriberto Cruz-Martínez ◽  
Dora I. Medina
Keyword(s):  
Carbon Monoxide ◽  
High Performance ◽  
Gas Sensing ◽  
Experimental Studies ◽  
Electrical Performance ◽  
Long Term Stability ◽  
Sensing Applications ◽  
Recent Advances ◽  
Sensitivity And Selectivity ◽  
Doped Zno

Monitoring and detecting carbon monoxide (CO) are critical because this gas is toxic and harmful to the ecosystem. In this respect, designing high-performance gas sensors for CO detection is necessary. Zinc oxide-based materials are promising for use as CO sensors, owing to their good sensing response, electrical performance, cost-effectiveness, long-term stability, low power consumption, ease of manufacturing, chemical stability, and non-toxicity. Nevertheless, further progress in gas sensing requires improving the selectivity and sensitivity, and lowering the operating temperature. Recently, different strategies have been implemented to improve the sensitivity and selectivity of ZnO to CO, highlighting the doping of ZnO. Many studies concluded that doped ZnO demonstrates better sensing properties than those of undoped ZnO in detecting CO. Therefore, in this review, we analyze and discuss, in detail, the recent advances in doped ZnO for CO sensing applications. First, experimental studies on ZnO doped with transition metals, boron group elements, and alkaline earth metals as CO sensors are comprehensively reviewed. We then focused on analyzing theoretical and combined experimental–theoretical studies. Finally, we present the conclusions and some perspectives for future investigations in the context of advancements in CO sensing using doped ZnO, which include room-temperature gas sensing.

scholarly journals Computational Molecular Modeling of Transport Processes in Nanoporous Membranes

Processes ◽  
2018 ◽  
Vol 6 (8) ◽  
pp. 124 ◽  
Author(s):  
Kevin Hinkle ◽  
Xiaoyu Wang ◽  
Xuehong Gu ◽  
Cynthia Jameson ◽  
Sohail Murad
Keyword(s):  
Molecular Modeling ◽  
Temporal Resolution ◽  
High Performance ◽  
Low Cost ◽  
Membrane Surface ◽  
Experimental Studies ◽  
Nanoporous Materials ◽  
Transport Processes ◽  
Zeolite Membrane ◽  
Nanoporous Membranes

In this report we have discussed the important role of molecular modeling, especially the use of the molecular dynamics method, in investigating transport processes in nanoporous materials such as membranes. With the availability of high performance computers, molecular modeling can now be used to study rather complex systems at a fraction of the cost or time requirements of experimental studies. Molecular modeling techniques have the advantage of being able to access spatial and temporal resolution which are difficult to reach in experimental studies. For example, sub-Angstrom level spatial resolution is very accessible as is sub-femtosecond temporal resolution. Due to these advantages, simulation can play two important roles: Firstly because of the increased spatial and temporal resolution, it can help understand phenomena not well understood. As an example, we discuss the study of reverse osmosis processes. Before simulations were used it was thought the separation of water from salt was purely a coulombic phenomenon. However, by applying molecular simulation techniques, it was clearly demonstrated that the solvation of ions made the separation in effect a steric separation and it was the flux which was strongly affected by the coulombic interactions between water and the membrane surface. Additionally, because of their relatively low cost and quick turnaround (by using multiple processor systems now increasingly available) simulations can be a useful screening tool to identify membranes for a potential application. To this end, we have described our studies in determining the most suitable zeolite membrane for redox flow battery applications. As computing facilities become more widely available and new computational methods are developed, we believe molecular modeling will become a key tool in the study of transport processes in nanoporous materials.

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