2018
DOI: 10.1007/s10064-018-1326-2
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Predicting the Young’s Modulus of granites using the Bayesian model selection approach

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Cited by 17 publications
(4 citation statements)
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“…FIGURE 7 | Relationship between the pressure change caused by the magma solidification and the ultimate load of the overlying crust. P c (max) and P c (min) are obtained from the average maximum and minimum Young's moduli of the main components of the crust (basalt and granite), respectively (Schultz, 1993;Yang et al, 2019). contributes to power plate tectonics and other geologic movements.…”
Section: Discussionmentioning
confidence: 99%
“…FIGURE 7 | Relationship between the pressure change caused by the magma solidification and the ultimate load of the overlying crust. P c (max) and P c (min) are obtained from the average maximum and minimum Young's moduli of the main components of the crust (basalt and granite), respectively (Schultz, 1993;Yang et al, 2019). contributes to power plate tectonics and other geologic movements.…”
Section: Discussionmentioning
confidence: 99%
“…Singh et al (2012) introduced the ANFIS architecture as a method for predicting rock E. Cao et al (2022) adopted an innovative approach by combining XGBoost and the Firefly Algorithm (FA) in supervised ML to predict E. The results showed that this novel method was effective. Yang et al (2019) used a Bayesian method to predict intact granite's E, and the model produced suitable predictions. Rastegarnia et al (2018) predicted the mechanical characteristics of sedimentary rocks, especially UCS and E, using ANN with R 2 of 0.99 and 0.97, respectively.…”
Section: Related Workmentioning
confidence: 99%
“…References Models Inputs Number of Samples Majdi and Beiki [30] GA-ANN UCS, GSI, RQD, ρ, n, NJ 120 Dehghan et al [29] ANN V p , Is (50) , R n , n 30 Khandelwal and Singh [31] ANN UCS, BTS 120 Ocak and Seker [32] ANN UCS, γ 195 Armaghani et al [17] ANFIS ρ, V p , content of Qtz, Kpr, Plg, and Bi 45 Bejarbaneh et al [33] ANN Is (50) , V p , R n 96 Saedi et al [34] ANFIS BPI, BTS, V p , Is (50) 120 Rezaei [35] MFIS H, ρ, n, DI 50 Yang et al [36] Bayesian I S(50) , R n , V p , n, UCS 71 Acar and Kaya [26] LS-SVM V p , γ, Is (50) , BTS 575 Cao et al [23] XGBoost-firefly ρ, V p , content of Qtz, Kpr, Plg, Bi 45 Pappalardo and Mineo [27] ANN n, γ, V p , E dyn , UCS / Meng and Wu [24] RF PF, SF, IAF, TF, NF / Abdi et al [25] RF n, ρ, V p , Id In comparison to other methods, ML approaches can yield dependable results by establishing the nonlinear relationship between input and output variables [37,38]. It is a promising method for estimating the E of rock.…”
Section: Yearmentioning
confidence: 99%