A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Monjezi, Masoud; Elangovan, K.; Razifard, M.

doi:10.1007/s10706-012-9510-9

Cited by 93 publications

(13 citation statements)

References 47 publications

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“…The cosine amplitude method has also been used by various researchers in the field of geotechnical engineering. For instance, Monjezi et al (2012) studied the influence of various parameters on the uniaxial compressive strength of the rock. Momeni et al (2014) determined the parameters influencing the bearing capacity of stockpile using the cosine amplitude approach.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

Lawal

Aladejare

Onifade

et al. 2020

Int J Coal Sci Technol

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The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects. The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly. However, limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials. In this study, some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed. Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models. Dependent variables for multiple prediction models were selected as carbon, hydrogen, and oxygen. Using volatile matter, fixed carbon, moisture and ash contents as independent variables, three different prediction models were developed for each dependent parameter using ANFIS, ANN, and MLR. In addition, a routine for selecting the best predictive model was suggested in the study. The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory. Therefore, the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.

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Section: Sensitivity Analysismentioning

confidence: 99%

Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

Lawal

Aladejare

Onifade

et al. 2020

Int J Coal Sci Technol

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“…In addition, the UCS test is a destructive, expensive, and time-consuming method as reported by many researchers (9)(10)(11)(12)(13)(14). For these reasons, the correlation between UCS and E t with the non-destructive or simple tests that require no sample preparation is more desired (15)(16)(17). Different empirical models have been developed to estimate the UCS and E t depending on simple or non-destructive tests.…”

Section: Introductionmentioning

confidence: 99%

Assessing the uniaxial compressive strength and tangent Young’s modulus of basalt rock using the Leeb rebound hardness test

2020

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Basalt was used as an ornamental stone in any historic and ancient cities in Jordan. Measuring the uniaxial compressive strength (UCS) and the tangent Young’s modulus (Et) in the laboratory requires premium quality specimens with special core dimensions. This research focuses on correlations both UCS and Et with Leeb Rebound Hardness Test (LRH). In the laboratory, UCS, Et, and LRH were performed on 90 core samples extracted from 30 different rock boulders collected from the neighboring area of Umm al-Jimal, a historic city in northeastern Jordan. A strong power correlation with (R2 = 0.888, RMSE = 5.464) was found between non-destructive LRH value and UCS. On the other hand, a moderately strong linear regression with (R2 = 0.792, RMSE = 4.661) was found between Et and LRH. In conclusion, non-destructive LRHs can be used as indictors for evaluating both UCS and Et during the restoration of the historic city Umm al-Jimal and the rehabilitation of other existing structures.

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“…In rock and mining engineering, multiple regression analysis is popular as it has been used by researchers to establish regression models for the prediction of different rock properties (Gokceoglu and Zorlu 2004;Zorlu et al 2008;Monjezi et al 2012;Mishra and Basu 2013) The prediction models for the Brazilian tensile strength (Eqs. 1 and 2) are logical, because the variable (BTS) increases as the independent variables (n, w c ) decrease.…”

Section: Multiple Regressionmentioning

confidence: 99%

Characterization of the Petrographic and Physicomechanical Properties of Rocks from Otanmäki, Finland

Aladejare

2020

Geotech Geol Eng

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Intact rock properties are important for mining and geotechnical engineering because they are important design parameters for tunnels, rock foundations, rock slopes, among others. Some of the properties are also used as input parameters in some rock mass classification systems. Therefore, as rock properties are site-specific, there is need to investigate rock properties at a site especially when such site is used for engineering purpose. This paper documents the petrographic and X-ray fluorescence analyses, and physical and mechanical tests conducted to estimate the physical and mechanical properties of the two rock types (i.e. gabbro and granite) selected from quarries at Otanmäki, Finland. Mineral composition, grain size and chemical composition, water content, porosity, Brazilian tensile strength, uniaxial compressive strength, Young’s modulus, and Poisson ratio of the rock types were determined. Both gabbro and granite samples have fine to medium grained texture. The results of laboratory experiments conducted show that rock properties investigated have low to high variability. Simple and multiple regression analyses were performed and useful predictive models for estimating Brazilian tensile strength from physical test results were developed and validated.

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A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Cited by 93 publications

References 47 publications

Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

Assessing the uniaxial compressive strength and tangent Young’s modulus of basalt rock using the Leeb rebound hardness test

Characterization of the Petrographic and Physicomechanical Properties of Rocks from Otanmäki, Finland

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