Empirical correlations between uniaxial compressive strength and density on the basis of lithology: implications from statistical and machine learning assessments

Rahman, Tabish; Sarkar, Kripamoy

doi:10.1007/s12145-023-00969-x

Earth Sci Inform

2023

DOI: 10.1007/s12145-023-00969-x

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Empirical correlations between uniaxial compressive strength and density on the basis of lithology: implications from statistical and machine learning assessments

Tabish Rahman

Kripamoy Sarkar

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Cited by 4 publications

References 34 publications

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Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Liu,

Sun,

Wang

et al. 2024

Earth Sci Inform

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The elastic modulus is one of the important parameters for analyzing the stability of engineering projects, especially dam sites. In the current study, the effect of physical properties, quartz, fragment, and feldspar percentages, and dynamic Young’s modulus (DYM) on the static Young’s modulus (SYM) of the various types of sandstones was assessed. These investigations were conducted through simple and multivariate regression, support vector regression, adaptive neuro-fuzzy inference system, and backpropagation multilayer perceptron. The XRD and thin section results showed that the studied samples were classified as arenite, litharenite, and feldspathic litharenite. The low resistance of the arenite type is mainly due to the presence of sulfate cement, clay minerals, high porosity, and carbonate fragments in this type. Examining the fracture patterns of these sandstones in different resistance ranges showed that at low values of resistance, the fracture pattern is mainly of simple shear type, which changes to multiple extension types with increasing compressive strength. Among the influencing factors, the percentage of quartz has the greatest effect on SYM. A comparison of the methods' performance based on CPM and error values in estimating SYM revealed that SVR (R2 = 0.98, RMSE = 0.11GPa, CPM = + 1.84) outperformed other methods in terms of accuracy. The average difference between predicted SYM using intelligent methods and measured SYM value was less than 0.05% which indicates the efficiency of the used methods in estimating SYM.

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Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Liu,

Sun,

Wang

et al. 2024

Earth Sci Inform

View full text Add to dashboard Cite

show abstract

Weathering effects on granitic rocks in North Pakistan: petrographic insights, strength classifications, and construction suitability

Asif,

Sajid,

Ahmed

et al. 2024

Environ Earth Sci

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Correlations Between Dynamic Elastic Properties and P-Wave Velocity for Different Rock Types

Rahman,

Sarkar,

Sahu

2023

Indian Geotech J

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Empirical correlations between uniaxial compressive strength and density on the basis of lithology: implications from statistical and machine learning assessments

Cited by 4 publications

References 34 publications

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models

Weathering effects on granitic rocks in North Pakistan: petrographic insights, strength classifications, and construction suitability

Correlations Between Dynamic Elastic Properties and P-Wave Velocity for Different Rock Types

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