Analysis of Obstacle of Development and Application that Coal Gangue Used in China Road Engineering

2014 ◽  
Vol 900 ◽  
pp. 510-513 ◽  
Author(s):  
Qing Qing Ma ◽  
Hua Liu
Keyword(s):  
Principal Component ◽  
Liquid Limit ◽  
Coal Gangue ◽  
Plastic Limit ◽  
Engineering Material ◽  
Road Engineering ◽  
Slake Durability Index ◽  
Durability Index ◽  
Limit Index ◽  
Major Factors

Coal gangue as road engineering material can sovle the problems of energy saving and environmental protection.Experimental datas of grain size distribution,crushing value,slake durability index,expansivity,liquid limit index,plastic limit index and water absorption showed that its own characteristics are in line with the requirements of road engineering materials. There are successful cases of application of coal gangue in road constructions both here and abroad. Principal component analysis is used to identify four major factors of coal gangue applications in road construction.Put forward 5 urgent problems to be solved and give a few corresponding suggestions.

The Daxinganling Island Taliks Sheet Permafrost Feature

2013 ◽  
Vol 368-370 ◽  
pp. 1816-1820
Author(s):  
Lin Ding ◽  
Yan Jie Liu ◽  
Yi Mei Min
Keyword(s):  
Field Investigation ◽  
Liquid Limit ◽  
Frozen Soil ◽  
Frost Heave ◽  
Theoretic Analysis ◽  
Plastic Limit ◽  
Geological Conditions ◽  
Natural Water Content ◽  
Thaw Depth ◽  
Limit Index

In order to study the features of permafrost in Daxinganling, based on t he methods of field investigation and borehole drilling, as w ell as theoretic analysis and the effects of frozen soil geological conditions, topography features, frozen soil type and permafrost classification the freeze-thaw depth(m),main physical indicators of the Frost soil, natural water content, plastic limit Liquid limit index were analysis .the thaw settlement and frost heave were studied in this paper.

scholarly journals Finite-difference based response surface methodology to optimize tailgate support systems in longwall coal mining

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satar Mahdevari ◽  
Mohammad Hayati
Keyword(s):  
Compressive Strength ◽  
Response Surface Methodology ◽  
Rock Mass ◽  
Finite Difference ◽  
Longwall Mining ◽  
Support Systems ◽  
Rock Mass Rating ◽  
Slake Durability Index ◽  
Proposed Model ◽  
Durability Index

AbstractDesigning a suitable support system is of great importance in longwall mining to ensure the safe and stable working conditions over the entire life of the mine. In high-speed mechanized longwall mining, the most vulnerable zones to failure are roof strata in the vicinity of the tailgate roadway and T-junctions. Severe roof displacements are occurred in the tailgate roadway due to the high-stress concentrations around the exposed roof span. In this respect, Response Surface Methodology (RSM) was utilized to optimize tailgate support systems in the Tabas longwall coal mine, northeast of Iran. The nine geomechanical parameters were obtained through the field and laboratory studies including density, uniaxial compressive strength, angle of internal friction, cohesion, shear strength, tensile strength, Young’s modulus, slake durability index, and rock mass rating. A design of experiment was developed through considering a Central Composite Design (CCD) on the independent variables. The 149 experiments are resulted based on the output of CCD, and were introduced to a software package of finite difference numerical method to calculate the maximum roof displacements (dmax) in each experiment as the response of design. Therefore, the geomechanical variables are merged and consolidated into a modified quadratic equation for prediction of the dmax. The proposed model was executed in four approaches of linear, two-factor interaction, quadratic, and cubic. The best squared correlation coefficient was obtained as 0.96. The prediction capability of the model was examined by testing on some unseen real data that were monitored at the mine. The proposed model appears to give a high goodness of fit with the accuracy of 0.90. These results indicate the accuracy and reliability of the developed model, which may be considered as a reliable tool for optimizing or redesigning the support systems in longwall tailgates. Analysis of variance (ANOVA) was performed to identify the key variables affecting the dmax, and to recognize their pairwise interaction effects. The key parameters influencing the dmax are respectively found to be slake durability index, Young’s modulus, uniaxial compressive strength, and rock mass rating.

Effects of Laumontite Hydration/Dehydration on Swelling Deformation and Slake Durability of Altered Granodiorite

2020 ◽  
Vol 26 (4) ◽  
pp. 507-516
Author(s):  
Junsong Yan ◽  
Junhui Shen ◽  
Kaizhen Zhang ◽  
Jianjun Xu ◽  
Weifeng Duan ◽  
...  
Keyword(s):  
Axial Strain ◽  
Test Results ◽  
X Ray Diffraction ◽  
Dam Foundation ◽  
Hydro Power ◽  
Slake Durability Index ◽  
Slake Durability ◽  
Dehydration Reactions ◽  
Short Period ◽  
Durability Index

ABSTRACT The mineral laumontite can undergo hydration/dehydration reactions at room temperature. The hydration/dehydration produces a 3 to 6 percent volume change in the unit cell. The effects of laumontite hydration/dehydration on swelling and slake durability were investigated using altered granodiorite containing laumontite from the dam foundation of Yangfanggou Hydro Power Station, Sichuan, China. The occurrence of laumontite in altered rocks was first determined by petrological analysis. Typical samples were then collected for laboratory X-ray diffraction (XRD) analyses, free swelling tests, and slake durability index (SDI) tests. The test results were analyzed to determine the quantitative relationships between laumontite content, maximum axial strain, and slake durability index. We found that hydration of laumontite led to rock swelling. As laumontite content increased, maximum axial strain increased linearly; if water penetrated the rock quickly, swelling occurred over a short period. The hydration/dehydration of laumontite decreased slake durability of the rock; the SDI decreased approximately linearly as laumontite content increased.

scholarly journals Person - job fi t and the work commitment of IT Personnel

2016 ◽  
Vol 26 (2) ◽  
pp. 218 ◽  
Author(s):  
Therasa C. ◽  
C. Vijayabanu
Keyword(s):  
Regression Analysis ◽  
Principal Component ◽  
Attrition Rate ◽  
P Value ◽  
Work Commitment ◽  
Key Factors ◽  
Coeffi Cients ◽  
Work Autonomy ◽  
Major Factors ◽  
The Impact

Introduction: There have been given a much higher importance to employee commitment and retention since India is experiencing the highest attrition rate globally Economic Times, 2015. Hence, considering the factors of Person-job fi t to interpret the impact towards work commitment is very well essential, especially in the current scenario. Work Commitment is a vital element in any organization which has outstanding impact on productivity and functioning and hence it is very much vital to have a committed workforce which is necessary in this competitive environment and tight labour market. In the same way, there is considerable amount of evidence that if P-J fi t is high then it will have a direct impact on organization commitment also. Person-job fi t is the compatibility between person’s competency and abilities and the requirements of the job Zheng et al.2. If there exists a mismatch between person-job fi t then the consequences might result in poor work commitment, low job satisfaction and extremely lower involvement in the job. Objective: This study analyzed the key factors that contribute to Person- job compatibility among IT workers and also analyzed the relationship and impact of Person- job compatibility towards work commitment. Methods: Exploratory Factor Analysis (EFA) was used to fi lter the key factors initially, followed by a linear regression technique to determine the impact of Person- job compatibility factors in work commitment on a sample of 300 employees. EFA used Principal Component analysis for extraction and Promax for rotation. Finally regression analysis was carried out to predict the work commitment through statistically significant person-job compatibility variables. Results: The impact of person-job compatibility on work commitment was studied through regression analysis and it imply that for every unit increase in HR Policy, a 0.52 (unstandardised coeffi cients) increase in work commitment is predicted and it has been turned out as a most impacting variable to predict work commitment. The coeffi cients for Relationship (B =.330, sig =.000), HR Policies (B =.519, sig =.000), Pay and Benefi ts (B =.386, sig =.000) and Employee Growth (B =.290, sig =.001) were statistically significant, since its p-value is .000 which is smaller than .05. The coeffi cients for Work Autonomy (B =.154, sig = .081) was not statistically signifi cant, since its p-value is 0.081 which is greater than.05. Conclusion: The major factors responsible for creating work commitment among IT employees are Relationship, HR Policies and strategies, Pay and benefi ts, Work autonomy and Employee growth. The most contributing regressors which accounts for creating work commitment are HR policies, Pay and benefi ts, Employee growth and work autonomy.

scholarly journals Penerapan Kansei Engineering dalam Perbandingan Desain Aplikasi Mobile Marketplace di Indonesia

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Nucky Vilano ◽  
Setia Budi
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Mobile Application ◽  
Principal Component ◽  
Kansei Engineering ◽  
Multivariate Statistical ◽  
Christian University ◽  
Emotional Factors ◽  
Coefficient Corrélation ◽  
Major Factors

The company's application design is very important because it displays the company's image and to attract more users to purchase/utilize the application. This research applies Kansei Engineering Method to analyze the emotion or feelings of the user towards the design of a mobile application interface. Six Kansei Words and three specimens are utilised in this research, where Kansei words are selected from words related to user experience. The participants of this research consist of 54 students from Maranatha Christian University. Participants’ responses are studied using multivariate statistical analysis (e.g., Coefficient Correlation Analysis, Principal Component Analysis, and Factor Analysis). This study explores the emotional factors that occur in designing an application. This analysis shows that there are some major factors that greatly influence the design of a mobile application interface.

scholarly journals Prediction of Shear Strength of Soil Using Direct Shear Test and Support Vector Machine Model

2020 ◽  
Vol 14 (1) ◽  
pp. 41-50 ◽  
Author(s):  
Hai-Bang Ly ◽  
Binh Thai Pham
Keyword(s):  
Support Vector Machine ◽  
Shear Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Liquid Limit ◽  
Support Vector ◽  
Plastic Limit ◽  
Svm Model ◽  
Soil Shear Strength

Background: Shear strength of soil, the magnitude of shear stress that a soil can maintain, is an important factor in geotechnical engineering. Objective: The main objective of this study is dedicated to the development of a machine learning algorithm, namely Support Vector Machine (SVM) to predict the shear strength of soil based on 6 input variables such as clay content, moisture content, specific gravity, void ratio, liquid limit and plastic limit. Methods: An important number of experimental measurements, including more than 500 samples was gathered from the Long Phu 1 power plant project’s technical reports. The accuracy of the proposed SVM was evaluated using statistical indicators such as the coefficient of correlation (R), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) over a number of 200 simulations taking into account the random sampling effect. Finally, the most accurate SVM model was used to interpret the prediction results due to Partial Dependence Plots (PDP). Results: Validation results showed that SVM model performed well for prediction of soil shear strength (R = 0.9 to 0.95), and the moisture content, liquid limit and plastic limit were found as the three most affecting features to the prediction of soil shear strength. Conclusion: This study might help in quick and accurate prediction of soil shear strength for practical purposes in civil engineering.

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