scholarly journals The effect of clay content on the relation between uniaxial compressive strength and needle penetration index for clay-bearing rocks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sair Kahraman ◽  
A. Sercan Aloglu ◽  
Egemen Saygin ◽  
Bilal Aydin
Keyword(s):  
Compressive Strength ◽  
Regression Analysis ◽  
Multiple Regression ◽  
Uniaxial Compressive Strength ◽  
Clay Content ◽  
Xrd Analysis ◽  
Multiple Regression Model ◽  
Penetration Index ◽  
Needle Penetration ◽  
Needle Penetration Index

AbstractThe needle penetration index (NPI) test is a non-destructive test that is applicable both in the field and laboratory, and does not require any special sample preparation. This test has been used for the estimation of physico-mechanical properties of soft rocks. In this study, the influence of the clay content on the relation between uniaxial compressive strength (UCS) and the NPI has been investigated for some clay-bearing rocks. The needle penetration tests were carried out at nine different gallery faces during the Cayirhan Coal Mine excavations, and the NPI values were calculated. Claystone, clayey limestone and clay blocks were collected from the locations on which the NPI tests were performed for the determination of rock strength and clay contents. The clay contents and clay fractions of the samples were determined using XRD analysis. A strong correlation has been found between the UCS and the NPI, but some of the data points were scattered. Strong correlations were also found between the NPI and both the total clay content and the smectite content. The UCS values were also strongly correlated to the total clay content and the smectite content. A multiple regression analysis was performed to determine the influence of clay content on the UCS-NPI relation and a very strong model was derived. The correlation coefficient of the multiple regression model is fairly higher than that of the UCS-NPI relation derived by using simple regression analysis. Concluding remark is that the clay content significantly affects the UCS-NPI relation in clay-bearing rocks.

Predicting Compressive Strength of Recycled Aggregate Concrete by Multiple Regression Analysis

2012 ◽  
Vol 253-255 ◽  
pp. 546-549 ◽  
Author(s):  
Yoon Seok Shin ◽  
Gwang Hee Kim
Keyword(s):  
Compressive Strength ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Construction Industry ◽  
Multiple Regression Model ◽  
Recycled Aggregate Concrete ◽  
Recycled Aggregate ◽  
Average Error ◽  
Aggregate Concrete

Today, efforts are underway in a number of areas to conserve the environment and protect natural resource. In the construction industry, many researchers have studied the development of new concrete using recycled aggregate (RA). This research proposes a multiple regression model (MRM) for predicting the compressive strength of recycled aggregate concrete (RAC). The compressive strength data of 85 specimens of RAC strengths were used for constructing and evaluating the prediction model. The average error rate of the constructed MRM evaluation is 7.18 percent. This result will be useful for predicting the compressive strength of RAC using multiple regression analysis.

scholarly journals Determination of Emissivity of Brass Alloy using Infrared Thermographic Technique

2018 ◽  
Vol 65 (3) ◽  
pp. 115-121
Author(s):  
Zorana Lanc ◽  
Milan Zeljković ◽  
Aleksandar Živković ◽  
Branko Štrbac ◽  
Miodrag Hadžistević
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Multiple Regression ◽  
Strong Correlation ◽  
Three Dimensional ◽  
Multiple Regression Model ◽  
Infrared Images ◽  
Cooling Process ◽  
Brass Alloy

Abstract This paper presents the experimental determination of the dependence of emissivity of brass on surface roughness and temperature. The investigation was conducted using the infrared thermographic technique on brass alloy C27200 workpieces with different degrees of surface roughness, during the continuous cooling process. The results obtained showed that the emissivity of the chosen brass alloy increases with greater surface roughness and decreases during the cooling process, its value ranging from 0.07 to 0.19. It was concluded that surface roughness has a greater influence on the increase of the emissivity at higher temperatures, which can be seen in the three-dimensional infrared images. Multiple regression analysis confirmed a strong correlation between the examined parameters and the emissivity, and an original multiple regression model was determined.

Research on Related Problems of PM2.5 in Air

2014 ◽  
Vol 644-650 ◽  
pp. 5319-5324
Author(s):  
Tian Jiu Leng
Keyword(s):  
Regression Analysis ◽  
Boundary Conditions ◽  
Regression Model ◽  
Multiple Regression ◽  
Stepwise Regression ◽  
Multiple Regression Model ◽  
Stepwise Regression Analysis ◽  
Spatial Evolution ◽  
Analysis Method ◽  
Relevant Factors

In this paper, the relevant factors of PM2.5 and the degree of correlation between them were analyzed.The multiple regression model was established using stepwise regression analysis method and the temporal spatial evolution of PM2.5 was obtained by setting the initial and boundary conditions.

scholarly journals Relationship of soil properties to fractionation, bioavailability and mobility of lead and zinc in soil

2008 ◽  
Vol 53 (No. 5) ◽  
pp. 225-238 ◽  
Author(s):  
N. Finžgar ◽  
P. Tlustoš ◽  
D. Leštan
Keyword(s):  
Organic Matter ◽  
Regression Analysis ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Multiple Regression ◽  
Contaminated Soils ◽  
Organic Matter Content ◽  
Clay Content ◽  
Exchange Capacity ◽  
Matter Content

Sequential extractions, metal uptake by <i>Taraxacum officinale</i>, Ruby&rsquo;s physiologically based extraction test (PBET) and toxicity characteristic leaching procedure (TCLP), were used to assess the risk of Pb and Zn in contaminated soils, and to determine relationships among soil characteristics, heavy metals soil fractionation, bioavailability and leachability. Regression analysis using linear and 2nd order polynomial models indicated relationships between Pb and Zn contamination and soil properties, although of small significance (<i>P</i> < 0.05). Statistically highly significant correlations (<i>P</i> < 0.001) were obtained using multiple regression analysis. A correlation between soil cation exchange capacity (CEC) and soil organic matter and clay content was expected. The proportion of Pb in the PBET intestinal phase correlated with total soil Pb and Pb bound to soil oxides and the organic matter fraction. The leachable Pb, extracted with TCLP, correlated with the Pb bound to carbonates and soil organic matter content (<i>R</i><sup>2</sup> = 69%). No highly significant correlations (<i>P</i> < 0.001) for Zn with soil properties or Zn fractionation were obtained using multiple regression.

scholarly journals Multiple Regression Analysis of Reading Performance Data from Twin Pairs with Reading Difficulties and Nontwin Siblings: The Augmented Model

2012 ◽  
Vol 15 (1) ◽  
pp. 116-119 ◽  
Author(s):  
Sally J. Wadsworth ◽  
Richard K. Olson ◽  
Erik G. Willcutt ◽  
John C. DeFries
Keyword(s):  
Regression Analysis ◽  
Multiple Regression ◽  
Environmental Influences ◽  
Reading Difficulties ◽  
Reading Performance ◽  
Multiple Regression Model ◽  
Performance Data ◽  
Extended Model ◽  
Sibling Pairs ◽  
The Difference

The augmented multiple regression model for the analysis of data from selected twin pairs was extended to facilitate analyses of data from twin pairs and nontwin siblings. Fitting this extended model to data from both selected twin pairs and siblings yields direct estimates of heritability (h2) and the difference between environmental influences shared by members of twin pairs and those of sib or twin–sib pairs (i.e., c2(t) – c2 (s)). When this model was fitted to reading performance data from 293 monozygotic and 436 dizygotic pairs selected for reading difficulties, and 291 of their nontwin siblings, h2 = .48 ± .22, p = .03, and c2 (t) – c2 (s) = .22 ± .12, p = .06. Although the test for differential shared environmental influences is only marginally significant, the results of this analysis suggest that environmental influences on reading performance that are shared by members of twin pairs (.36) may be substantially greater than those for less contemporaneous twin–sibling pairs (.14).

scholarly journals A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

2020 ◽  
Vol 20 (6) ◽  
pp. 311-321
Author(s):  
YeoungRok Oh ◽  
Gyumin Lee ◽  
Kyung Soo Jun ◽  
Wooyeon Sunwoo ◽  
SeungWoo Baek ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Snow Depth ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Independent Variables ◽  
Fresh Snow ◽  
Humidity Range

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

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