Prediction of engineering properties of rocks from microscopic data

Özçelik, Yılmaz; Bayram, Fatih; Yaşitli, Nazmi Erhan

doi:10.1007/s12517-012-0625-3

Cited by 31 publications

(11 citation statements)

References 20 publications

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“…Thus, attention must be paid to the indirect evaluation of E through the use of rock index tests. Many researchers have established prediction models to overcome these shortcomings by employing soft computing methods such as artificial neural network (ANN), multiple regression analysis (MRA) and other novel machine learning approaches (Lindquist et al, 1994;Singh and Dubey, 2000;Tiryaki, 2008;OzcelikBayram et al, 2013;Abdi et al, 2018;Teymen and Mengüç, 2020;Cao et al, 2021;Yang et al, 2020;Duan et al, 2020). Waqas et al used linear and nonlinear regression, regularization and ANFIS (using neuro-fuzzy inference system) to predict the E d of sedimentary rocks (Waqas and Ahmed, 2020) (Elkatatny et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

et al. 2021

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Young’s modulus (E) is essential for predicting the behavior of materials under stress and plays an important role in the stability of surface and subsurface structures. E has a wide range of applications in mining, geology, civil engineering, etc.; for example, coal and metal mines, tunnels, foundations, slopes, bridges, buildings, drilling, etc. This study developed a novel machine learning regression model, namely an extreme gradient boosting (XGBoost) to predict the influences of four inputs such as uniaxial compressive strength in MPa; density in g/cm3; p-wave velocity (Vp) in m/s; and s-wave velocity in m/s on two outputs, namely static Young’s modulus (Es) in GPa; and dynamic Young’s modulus (Ed) in GPa. Using a series of basic statistical analysis tools, the accompanying strengths of each input and each output were systematically examined to classify the most prevailing and significant input parameters. Then, two other models i.e., multiple linear regression (MLR) and artificial neural network (ANN) were employed to predict Es and Ed. Next, multiple linear regression and ANN were compared with XGBoost. The original dataset was allocated as 70% for the training stage and 30% for the testing stage for each model. To improve the performance of the developed models, an iterative 10-fold cross-validation method was used. Therefore, based on the results XGBoost model has revealed the best performance with high accuracy (Es: correlation coefficient (R2) = 0.998; Ed: R2 = 0.999 in the training stage; Es: R2 = 0.997; Ed: R2 = 0.999 in the testing stage), root mean square error (RMSE) (Es: RMSE = 0.0652; Ed: RMSE = 0.0062 in the training stage; Es: RMSE = 0.071; Ed: RMSE = 0.027 in the testing stage), RMSE-standard deviation ratio (RSR) index value (Es: RSR = 0.00238; Ed: RSR = 0.00023 in the training stage; Es: RSR = 0.00304; Ed: RSR = 0.001 in the testing stage) and variance accounts for (VAF) (Es: VAF = 99.71; Ed: VAF = 99.99 in the training stage; Es: VAF = 99.83; Ed: VAF = 99.94 in the testing stage) compared to the other developed models in this study. Using a novel machine learning approach, this study was able to deliver substitute elucidations for predicting Es and Ed parameters with suitable accuracy and runtime.

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Section: Introductionmentioning

confidence: 99%

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

et al. 2021

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“…Significant efforts had been made to use indirect non-destructive tests instead of direct destructive tests in geotechnical applications, such as tunnels, dams, rock excavation or drilling, roads, slope stability assessment, and building. This is due to the direct destructive tests, such as the unconfined compressive strength (UCS) test, which are mostly overworked, time consumed and more expensive, as it requires special sample preparation and depends on rigorous standards (Ceryan et al, 2013;Ozcelik, Bayram, & Yasitli, 2013).…”

Section: Introductionmentioning

confidence: 99%

Statistical Analyses and Geotechnical Evaluation of Nubia Sandstone, Golden Triangle Area, Egypt

Ismaiel¹,

Askalany²,

Ali³

2021

GEP

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“…Different scholars have investigated the effect of textural characteristics on strength parameters of rocks. These characteristics include composition of mineral, grain size and grain shape, degree of cementation and cementation material (Ozcelik et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effects of Textural Properties on the Strength Parameters of Marbles from North-Western Khyber Pakhtunkhwa, Pakistan

Muhammad

Hassan

Rehman

et al. 2021

IJEEG

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Marble is globally used as a natural stone for decorative and architectural purposes. Primary utilization of marble is as building and dimension stones. Mechanical properties and aesthetic aspects are major characteristics of marble and decisive factors for its selection and utilization. It is therefore imperative to evaluate the key strength properties i.e. Uniaxial Compressive Strength (UCS) and Uniaxial Tensile Strength (UTS) of marble before its utilization. These key strength parameters are dependent on textural features of marble. Present study investigates the effect of two key textural features i.e. grain size and grain shape on two key strength parameters i.e. UCS and UTS of marble samples taken from three different regions i.e. Buner, Chitral and Swat in the north western part of Khyber Pakhtunkhwa. Correlation and regression analysis between these textural properties and strength parameters revealed that prominent textural features of grain size and shape can be used as a quick indicator for assessment of strength parameters and as guideline for appropriate utilization of marble.

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Prediction of engineering properties of rocks from microscopic data

Cited by 31 publications

References 20 publications

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

Developing an XGBoost Regression Model for Predicting Young’s Modulus of Intact Sedimentary Rocks for the Stability of Surface and Subsurface Structures

Statistical Analyses and Geotechnical Evaluation of Nubia Sandstone, Golden Triangle Area, Egypt

Evaluating the Effects of Textural Properties on the Strength Parameters of Marbles from North-Western Khyber Pakhtunkhwa, Pakistan

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