2018
DOI: 10.1016/j.enggeo.2018.03.027
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Determination of thermal damage in rock specimen using intelligent techniques

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Cited by 44 publications
(18 citation statements)
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“…These predictions improved when using artificial neural network and adaptive neuro-fuzzy inference systems (coefficients of determination were close to 1) [40]. Such techniques and predictors were used to improve the prediction of thermal damage [41].…”
Section: Introductionmentioning
confidence: 99%
“…These predictions improved when using artificial neural network and adaptive neuro-fuzzy inference systems (coefficients of determination were close to 1) [40]. Such techniques and predictors were used to improve the prediction of thermal damage [41].…”
Section: Introductionmentioning
confidence: 99%
“…Li et al [8] proposed to use colorimetry as a simple but effective method to assess fire damage of rocks. The hardness is found to generally weaken as the temperature applied to the rocks is gradually increased [9][10][11][12]. Similarly, with an increase in the applied temperature, the P-wave velocity showed a decreasing trend [13][14][15].…”
Section: Introductionmentioning
confidence: 94%
“…To evaluate the performance of the fitting curves, the root mean square error (RMSE) and coefficient of determination (R 2 ) were introduced [30,91]. R 2 measures how well the association is between changes in two variables, and RMSE is used to find the sample standard deviation of the error between regressed and actual values.…”
Section: Peak Strength and Peak Axialmentioning
confidence: 99%