Prediction of uniaxial compressive strength of carbonate rocks and cement mortar using artificial neural network and multiple linear regressions

2020 ◽  
pp. 367-377
Author(s):  
Mohamed Abdelhedi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Carbonate Rocks ◽  
Cement Mortar ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions

scholarly journals Data on estimation for sodium absorption ratio: Using artificial neural network and multiple linear regressions

Data in Brief ◽  
2018 ◽  
Vol 20 ◽  
pp. 1462-1467 ◽  
Author(s):  
Majid Radfard ◽  
Hamed Soleimani ◽  
Samira Nabavi ◽  
Bayram Hashemzadeh ◽  
Hesam Akbari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sodium Absorption ◽  
Multiple Linear Regressions ◽  
Sodium Absorption Ratio ◽  
Absorption Ratio ◽  
Artificial Neural ◽  
Linear Regressions

scholarly journals Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

2019 ◽  
Vol 68 (11-12) ◽  
pp. 573-582 ◽  
Author(s):  
Naima Melzi ◽  
Hamid Zentou ◽  
Maamar Laidi ◽  
Salah Hanini ◽  
Yamina Ammi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Molecular Diffusion ◽  
Mean Squared Error ◽  
External Validation ◽  
Multiple Linear Regressions ◽  
Mean Squared Errors ◽  
Wide Range ◽  
Artificial Neural ◽  
Linear Regressions

In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).

Predictive Modeling of Uniaxial Compressive Strength of Rocks for Protecting Environment Using Artificial Neural Network

2021 ◽  
Author(s):  
Niaz Muhammad Shahani ◽  
Xigui Zheng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Sedimentary Rocks ◽  
Training Dataset ◽  
The Earth ◽  
Testing Dataset ◽  
Ann Models ◽  
Artificial Neural

Abstract Sedimentary rocks provide information on previous environments on the surface of the earth. As a result, they are the principal narrators of former climate, life, and important events on the surface of the earth. Complexity and expensiveness of direct destructive laboratory tests are adversely affects the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established artificial neural network (ANN) approach to predict uniaxial compressive strength (UCS) in MPa of soft sedimentary rocks using different input parameters i.e. dry density (ρd) in g/cm3; Brazilian tensile strength (BTS) in MPa; point load index (Is(50)) in MPa. The developed ANN models M1, M2 and M3 were divided into the overall dataset; 70% training dataset and 30% testing dataset; and 60% training dataset and 40% testing dataset respectively. In addition, multiple linear regression (MLR) was performed to compare with the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M3 ANN model with the highest coefficient of correlation (R2), the smallest root mean squared error (RMSE), the highest variance accounts for (VAF) and reliable a10-index was 0.99, 0.00060, 0.99 and 0.99 respectively at the testing dataset revealing ideal results and proposed as the best-fit prediction model for UCS of soft sedimentary rocks at the Thar Coalfield, Pakistan, among other developed models in this study. Moreover, by performing sensitivity analysis, it was determined that the BTS and Is(50) were the most influential parameters in predicting UCS.

scholarly journals The use of artificial neural network and multiple linear regressions for stock market forecasting

MATEMATIKA ◽  
2017 ◽  
Vol 33 (1) ◽  
pp. 1
Author(s):  
Abdu Masanawa Sagir ◽  
Saratha Sathasivan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Exchange ◽  
Multiple Linear Regressions ◽  
Ann Model ◽  
Network Parameter ◽  
Stock Market Forecasting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Linear Regressions

In the recent economic crises, one of the precise uniqueness that all stock markets have in common is the uncertainty. An attempt was made to forecast future index of the Malaysia Stock Exchange Market using artificial neural network (ANN) model and a traditional forecasting tool – Multiple Linear Regressions (MLR). This paper starts with a brief introduction of stock exchange of Malaysia, an overview of artificial neural network and machine learning models used for prediction. System design and data normalization using MINITAB software were described. Training algorithm, MLR Model and network parameter models were presented. Best training graphs showing the training, validation, test and all regression values were analyzed.

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