Abstract
In this research, some predictive models were constructed for estimating the slake durability index of sedimentary rocks. Sandstone, limestone, travertine and conglomerate were collected as studied rocks, and comprehensive laboratory investigations such as mineralogical study and geotechnical properties including dry unit weight, porosity, Schmidt rebound hardness, P-wave velocity, uniaxial compressive strength and slake durability index were determined based on standard procedures. Results of mineralogy studies and XRD analysis showed studied rock samples are dominantly composed of quartz and calcite with different textures. The durability test, up to three cycles, was performed in fluids with different pH conditions. Based on the results, the slake durability index is affected by the pH of the test fluids, and in initial cycles, the decreasing rate of slake durability index is higher than the end cycles. Also, in most of the samples, a constant pattern exists between the slake durability index and pH of the testing solutions so that the weight loss increases with decreasing pH from 7 to 4. Soft computing techniques including multiple regression analysis (MRA), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were developed for estimating SDI for pH = 7 and 4 conditions. Experimental equations were obtained by MRA with correlation coefficients from 0.74 to 0.85 that show porosity, Schmidt hardness, P-wave velocity and UCS are the good parameters for estimating the SDI2 of rocks. In order to evaluate the performance of predictive models, some statistical coefficients, including R, RMSE, VAF, MAPE and PI were also calculated. ANFIS models have the best coefficients, and the results demonstrate that the ANN models are efficient when compared to MRA. Therefore, all methods obtained acceptable results, but the ANN and ANFIS are more reliable methods for estimating the SDI of rocks.
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Abbreviations
- SS:
-
Sandstone sample
- LS:
-
Limestone sample
- TS:
-
Travertine sample
- CS:
-
Conglomerate sample
- γ dry :
-
Dry unit weight (g/cm3)
- n :
-
Porosity (%)
- H S :
-
Schmidt rebound hardness
- V P :
-
Primary wave velocity (m/s)
- UCS:
-
Uniaxial compressive strength (MPa)
- SDI:
-
Slake-durability index (%)
- XRD:
-
X-ray diffraction
- MRA:
-
Multiple regression analysis
- ANN:
-
Artificial neural network
- ANFIS:
-
Adaptive neuro-fuzzy inference system
- R :
-
Correlation coefficient
- R 2 :
-
Coefficient of determination
- RMSE:
-
Root mean square error
- VAF:
-
Coefficient values account for
- MAPE:
-
Mean absolute percentage error
- PI:
-
Performance index
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The author likes to express his thanks to Mr. P. Khajevand for the English editing.
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Highlights
• This study aims to evaluate the SDI of rocks in different pH conditions, including 7, 5.5 and 4, and assess the effect of petrography, physical and mechanical properties.
• Mineralogy and texture of the rocks were studied by thin section and XRD analysis; also, physical and mechanical properties including γdry, n, VP, HS and UCS were determined.
• The results indicated that the decreasing rate of SDI in the initial cycles is higher than the end cycles, and weight loss increases with decreasing the pH from 7 to 4.
• Proposed some experimental equations for estimating the SDI by multiple regression analysis for pH = 7 and 4 with correlation coefficients in the range of 0.74 to 0.85.
• Performance of MRA, ANN and ANFIS models was assessed by some statistical coefficients, including R, RMSE, VAF, MAPE and PI.
• The results indicated that the ANFIS models have the best coefficients and the ANN models are efficient when compared to MRA for estimating the SDI of the rocks.
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Khajevand, R. Soft computing approaches for evaluating the slake durability index of rocks. Arab J Geosci 15, 1698 (2022). https://doi.org/10.1007/s12517-022-10997-4
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DOI: https://doi.org/10.1007/s12517-022-10997-4