scholarly journals Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness

2020 ◽  
Vol 12 (6) ◽  
pp. 2229 ◽  
Author(s):  
Danial Jahed Armaghani ◽  
Panagiotis G. Asteris ◽  
Behnam Askarian ◽  
Mahdi Hasanipanah ◽  
Reza Tarinejad ◽  
...  
Keyword(s):  
Important Variable ◽  
Point Load ◽  
P Wave ◽  
Support Vector ◽  
P Wave Velocity ◽  
Svm Model ◽  
Rbf Kernel ◽  
Rebound Number ◽  
Important Input ◽  
Rock Brittleness

The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.

scholarly journals Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

2020 ◽  
Vol 10 (5) ◽  
pp. 1691 ◽  
Author(s):  
Deliang Sun ◽  
Mahshid Lonbani ◽  
Behnam Askarian ◽  
Danial Jahed Armaghani ◽  
Reza Tarinejad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Load ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Brittleness Index ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Ann Model ◽  
Learning Techniques ◽  
Rock Brittleness

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Prediction of The Uniaxial Compressive Strength of Rocks Materials

2018 ◽  
pp. 31-96 ◽  
Author(s):  
Nurcihan Ceryan ◽  
Nuray Korkmaz Can
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
P Wave ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Artifical Neural Network ◽  
P Wave Velocity ◽  
Soft Computing Techniques ◽  
Soft Computing Methods ◽  
Direct And Indirect Methods

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

scholarly journals Use of GA-ANN and GA-SVM for a QSPR study on aqueous solubility of pesticides

2020 ◽  
pp. 66-66
Author(s):  
Amel Bouakkadia ◽  
Noureddine Kertiou ◽  
Rana Amiri ◽  
Youssouf Driouche ◽  
Djelloul Messadi
Keyword(s):  
Pearson Correlation ◽  
Chemical Properties ◽  
Aqueous Solubility ◽  
Important Variable ◽  
Support Vector ◽  
Ann Model ◽  
Experimental Procedure ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Selection Of

The partitioning tendency of pesticides, in these study herbicides in particular, into different environmental compartments depends mainly of the physic-chemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure determining aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, every time associated with genetic algorithm (GA) selection of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series 80 herbicides. The values of log S of the studied compounds were well correlated with de descriptors. Considering the pertinent descriptors, a Pearson Correlation Squared (R2) coefficient of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

scholarly journals Correlation of P-wave velocity with mechanical and physical properties of limestone with statistical analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hasan Arman
Keyword(s):  
Physical Properties ◽  
Wave Velocity ◽  
Point Load ◽  
P Wave ◽  
Unit Weight ◽  
Empirical Equations ◽  
Thin Sections ◽  
X Ray ◽  
P Wave Velocity ◽  
Mechanical And Physical Properties

AbstractThis study aims to investigate the correlation between the P-wave velocity (Vp) and the mechanical and the physical properties of the limestone; Vp tests were conducted on over 320 limestone samples. Moreover, the effects of the mineralogical, textural, and chemical composition of limestone were also studied through thin sections, scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray fluorescence (XRF). The relationships between the Vp and the uniaxial compressive strength (UCS), point load index (PLI(Is(50)), 2nd cycle of slake durability index (Id2), natural unit weight (γn), specific gravity (Gs(c)), water absorption by weight (WA), and porosity (n) were estimated using representative empirical equations. The empirical equations were validated by Student’s t test that has indicated the existence of strong relationships between the mechanical and physical properties of the intact limestone with Vp; the calculated t-values were higher than the t-critical value. Furthermore, the results of previously available studies were compared with the results of this study in terms of the generated equations for Vp values and the slope of a 1:1 line, which was used to appraise the predicted and measured values. This study demonstrates that the UCS, PLI(Is(50)), Id2, γn, Gs(c), WA, and n values of an intact limestone can be predicted by using Vp, which is fast, easy, economical and nondestructive test.

scholarly journals Correlating Natural, Dry, and Saturated Ultrasonic Pulse Velocities with the Mechanical Properties of Rock for Various Sample Diameters

2020 ◽  
Vol 10 (24) ◽  
pp. 9134
Author(s):  
Hasan Arman ◽  
Safwan Paramban
Keyword(s):  
Mechanical Properties ◽  
Ultrasonic Pulse ◽  
Point Load ◽  
P Wave ◽  
Point Load Index ◽  
Sample Diameter ◽  
Wave Velocities ◽  
P Wave Velocity ◽  
Indirect Tensile ◽  
P Wave Velocities

P-wave velocity is employed in various fields of engineering to estimate the mechanical properties of rock, as its measurement is reliable, convenient, rapid, nondestructive, and economical. The present study aimed to (i) correlate natural, dry, and saturated P-wave velocities with the mechanical properties of limestone and (ii) investigate how the ultrasonic P-wave velocities and mechanical properties of limestone are affected by the sample diameter. This study reveals that P-wave velocities under different environmental conditions can be correlated with the mechanical properties of limestone. Further, the R-value variations with different P-wave velocities for a given sample diameter are (i) negligible in terms of the uniaxial compressive strength (UCS) excluding 63.2 mm, (ii) limited for the diametrical point load index (PLID) except for 53.9 mm, (iii) perceived in case of the axial point load index (PLIA) for 47.7 mm, (iv) observed for the indirect tensile strength (ITS), but generally insignificant, and (v) detected in terms of Schmidt hammer value (SHV) except for 47.7 mm.

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