Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of the uniaxial compressive strength and Brazilian tensile strength of weak conglomerate
Abstract Uniaxial compressive strength and tensile strength considered as important parameters in characterization of rock material in rock engineering. The necessary core samples cannot always be obtained from weak and block-in-matrix conglomeratic rock. For this reason, the predictive models can employed for the indirect estimation of mechanical parameters. The study investigated correlations uniaxial compressive strength and tensile strength with point load index. Numerous specimens of weak conglomerate were collected from different sites of dams in Iran. Predictive models include regression techniques and artificial neural network. To control performance of prediction capacity of equation, root mean square error and correlation coefficients were calculated. The correlation coefficients indices were calculated as 0.96 for the uniaxial compressive strength obtained from the regression model and 0.94 obtained from artificial neural network model; 0.605 for the tensile strength obtained from the regression model and 0.638 obtained from artificial neural network model.
Prediction of the uniaxial compressive strength and Brazilian tensile strength of weak conglomerate
Abstract Uniaxial compressive strength and tensile strength considered as important parameters in characterization of rock material in rock engineering. The necessary core samples cannot always be obtained from weak and block-in-matrix conglomeratic rock. For this reason, the predictive models can employed for the indirect estimation of mechanical parameters. The study investigated correlations uniaxial compressive strength and tensile strength with point load index. Numerous specimens of weak conglomerate were collected from different sites of dams in Iran. Predictive models include regression techniques and artificial neural network. To control performance of prediction capacity of equation, root mean square error and correlation coefficients were calculated. The correlation coefficients indices were calculated as 0.96 for the uniaxial compressive strength obtained from the regression model and 0.94 obtained from artificial neural network model; 0.605 for the tensile strength obtained from the regression model and 0.638 obtained from artificial neural network model.
Prediction of the uniaxial compressive strength and Brazilian tensile strength of weak conglomerate
Behnaz Minaeian (Autor:in) / Kaveh Ahangari (Autor:in)
2017
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Point Load Strength Index and Uniaxial Compressive Strength
| British Library Conference Proceedings | 1999
| British Library Online Contents | 2011