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Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests
Abstract Uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. Using standard laboratory tests to determine UCS is a difficult, expensive and time-consuming task. The main purpose of this study is to develop a general model for predicting UCS of limestone samples and to investigate the relationships among UCS, Schmidt hammer rebound and P-wave velocity (V P). For this reason, some samples of limestone rocks were collected from the southwestern Iran. In order to evaluate a correlation, the measured and predicted values were examined utilizing simple and multivariate regression techniques. In order to check the performance of the proposed equation, coefficient of determination (R 2), root-mean-square error, mean absolute percentage error, variance accounts for (VAF %), Akaike Information Criterion and performance index were determined. The results showed that the proposed equation by multivariate regression could be applied effectively to predict UCS from its combinations, i.e., ultrasonic pulse velocity and Schmidt hammer hardness. The results also showed that considering high prediction performance of the models developed, they can be used to perform preliminary stages of rock engineering assessments. It was evident that such prediction studies not only provide some practical tools but also contribute to better understanding of the main controlling index parameters of UCS of rocks.
Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests
Abstract Uniaxial compressive strength (UCS) of an intact rock is an important geotechnical parameter for engineering applications. Using standard laboratory tests to determine UCS is a difficult, expensive and time-consuming task. The main purpose of this study is to develop a general model for predicting UCS of limestone samples and to investigate the relationships among UCS, Schmidt hammer rebound and P-wave velocity (V P). For this reason, some samples of limestone rocks were collected from the southwestern Iran. In order to evaluate a correlation, the measured and predicted values were examined utilizing simple and multivariate regression techniques. In order to check the performance of the proposed equation, coefficient of determination (R 2), root-mean-square error, mean absolute percentage error, variance accounts for (VAF %), Akaike Information Criterion and performance index were determined. The results showed that the proposed equation by multivariate regression could be applied effectively to predict UCS from its combinations, i.e., ultrasonic pulse velocity and Schmidt hammer hardness. The results also showed that considering high prediction performance of the models developed, they can be used to perform preliminary stages of rock engineering assessments. It was evident that such prediction studies not only provide some practical tools but also contribute to better understanding of the main controlling index parameters of UCS of rocks.
Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests
Azimian, Abdolazim (author)
Acta Geotechnica ; 12 ; 321-333
2016-07-04
13 pages
Article (Journal)
Electronic Resource
English
Limestone , P-wave velocity (V P) , Schmidt hammer rebound (SHR) , Simple and multivariate regression , Uniaxial compressive strength Engineering , Geoengineering, Foundations, Hydraulics , Continuum Mechanics and Mechanics of Materials , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics , Structural Mechanics
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A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks
| Online Contents | 2012
A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks
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