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Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches
https://orcid.org/0000-0001-8758-8457
Abstract The determination of uniaxial compressive strength (UCS) is one of the most important mechanical properties of rocks. The direct measurement of UCS using laboratory methods is difficult, costly, and time-consuming. The main purpose of this research is to estimate the UCS of some sedimentary rocks by using multiple regression analysis (MRA), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Sandstone, limestone, travertine, and conglomerate samples were collected as studied rocks and some geotechnical tests including dry density, porosity, ultrasonic P-wave velocity (UPV), point load strength (PLS), Brazilian tensile strength (BTS), block punch strength (BPS) and uniaxial compressive strength were determined. Results show the average values of $ γ_{dry} $, n, UPV, PLS, BTS, BPS, and UCS were obtained 2.42 g/$ cm^{3} $, 5.77%, 4.30 km/s, 7.91, 5.19, 6.37 and 29.70 MPa, respectively. To evaluate the performance of MRA, ANN, and ANFIS models, some statistical coefficients, including R, RMSE, VAF, MAPE, and PI were calculated. Based on the results, the average values of R were obtained 0.82, 0.96, and 0.99 and RMSE were 7.43, 3.74, and 0.50 for the MRA, ANN, and ANFIS models, respectively. Comparing the results demonstrated that the ANFIS models have higher validity and the ANN models are more efficient than MRA. Also, by comparing the results of this research with previous studies, it was found that constructed models with better performance were obtained. Finally, concluded that the MRA, ANN, and ANFIS methods can be successfully employed for predicting the UCS of rocks.
Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches
https://orcid.org/0000-0001-8758-8457
Abstract The determination of uniaxial compressive strength (UCS) is one of the most important mechanical properties of rocks. The direct measurement of UCS using laboratory methods is difficult, costly, and time-consuming. The main purpose of this research is to estimate the UCS of some sedimentary rocks by using multiple regression analysis (MRA), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Sandstone, limestone, travertine, and conglomerate samples were collected as studied rocks and some geotechnical tests including dry density, porosity, ultrasonic P-wave velocity (UPV), point load strength (PLS), Brazilian tensile strength (BTS), block punch strength (BPS) and uniaxial compressive strength were determined. Results show the average values of $ γ_{dry} $, n, UPV, PLS, BTS, BPS, and UCS were obtained 2.42 g/$ cm^{3} $, 5.77%, 4.30 km/s, 7.91, 5.19, 6.37 and 29.70 MPa, respectively. To evaluate the performance of MRA, ANN, and ANFIS models, some statistical coefficients, including R, RMSE, VAF, MAPE, and PI were calculated. Based on the results, the average values of R were obtained 0.82, 0.96, and 0.99 and RMSE were 7.43, 3.74, and 0.50 for the MRA, ANN, and ANFIS models, respectively. Comparing the results demonstrated that the ANFIS models have higher validity and the ANN models are more efficient than MRA. Also, by comparing the results of this research with previous studies, it was found that constructed models with better performance were obtained. Finally, concluded that the MRA, ANN, and ANFIS methods can be successfully employed for predicting the UCS of rocks.
Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches
https://orcid.org/0000-0001-8758-8457
Abstract The determination of uniaxial compressive strength (UCS) is one of the most important mechanical properties of rocks. The direct measurement of UCS using laboratory methods is difficult, costly, and time-consuming. The main purpose of this research is to estimate the UCS of some sedimentary rocks by using multiple regression analysis (MRA), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS). Sandstone, limestone, travertine, and conglomerate samples were collected as studied rocks and some geotechnical tests including dry density, porosity, ultrasonic P-wave velocity (UPV), point load strength (PLS), Brazilian tensile strength (BTS), block punch strength (BPS) and uniaxial compressive strength were determined. Results show the average values of $ γ_{dry} $, n, UPV, PLS, BTS, BPS, and UCS were obtained 2.42 g/$ cm^{3} $, 5.77%, 4.30 km/s, 7.91, 5.19, 6.37 and 29.70 MPa, respectively. To evaluate the performance of MRA, ANN, and ANFIS models, some statistical coefficients, including R, RMSE, VAF, MAPE, and PI were calculated. Based on the results, the average values of R were obtained 0.82, 0.96, and 0.99 and RMSE were 7.43, 3.74, and 0.50 for the MRA, ANN, and ANFIS models, respectively. Comparing the results demonstrated that the ANFIS models have higher validity and the ANN models are more efficient than MRA. Also, by comparing the results of this research with previous studies, it was found that constructed models with better performance were obtained. Finally, concluded that the MRA, ANN, and ANFIS methods can be successfully employed for predicting the UCS of rocks.
Prediction of the Uniaxial Compressive Strength of Rocks by Soft Computing Approaches
Khajevand, Reza (author)
2023
Article (Journal)
Electronic Resource
English
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
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