A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock

Gökçeoğlu, Candan; Zorlu, Kıvanç

doi:10.1016/j.engappai.2003.11.006

Cited by 315 publications

(76 citation statements)

References 28 publications

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“…In fact, the coefficient of correlation between the measured and predicted values is a good indicator to check the prediction performance of the model (Gokceoglu and Zorlu 2004). In this study, variance VAF, represented by Eq.…”

Section: Resultsmentioning

confidence: 99%

The use of neural networks for CPT-based liquefaction screening

Erzin

Ecemis

2014

Bull Eng Geol Environ

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This study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand-silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand-silt mixes quite efficiently.

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Section: Resultsmentioning

confidence: 99%

The use of neural networks for CPT-based liquefaction screening

Erzin

Ecemis

2014

Bull Eng Geol Environ

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“…The validation model shows the correlation coefficient between the predicted and measured dry densities, which was found to be very high, with R value of 0.99 ( Figure 11). This model is the model used to check the performance of our ANN [20]. The network phase (i.e.…”

Section: Resultsmentioning

confidence: 99%

Using artificial neural network to predict dry density of soil from thermal conductivity

Sanuade

Adesina

Amosun

et al. 2017

Materials and Geoenvironment

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Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R 2 ) of 0.80. The validation of our model between the actual and predicted dry densities shows R 2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.

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“…Najznačajniji i najviše korišteni modeli koji rade na principu neizrazite logike i zaključivanja su Mamdani i Sugeno modeli. Gokceoglu i Zorlu su na bazi Mamdani modela izradili model za procjenu USC i E koji je te značajke procjenjivao na temelju istih ulaznih vrijednosti kao u modelima (4) i (5) [30]. Isti tip neizrazitog modela koristili su Karakus i Tutmez pri izradi modela za procjenu UCS.…”

Section: Modeli Utemeljeni Na Analizi Neizrazitih Skupovaunclassified

“…Ulazni podatci u model bili su rezultati laboratorijskih ispitivanja kao u modelu (6) [31]. Spomenuti istraživači su, uspoređujući neizraziti model s modelom višestruke regresije, zaključili kako neizraziti model daje bolju procjenu UCS [30,31]. Alvarez i Babuska izradili su model za procjenu UCS pomoću teorijskih postavki Takagi-Sugeno modela na temelju rezultata ispitivanja provedenih na 226 uzoraka intaktnog stijenskog materijala koji su pripadali pješčenjacima, vapnencima, dolomitima, granitima i grandioritima.…”

Section: Modeli Utemeljeni Na Analizi Neizrazitih Skupovaunclassified

Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti

Briševac¹,

Hrženjak²,

Buljan³

2016

JCE

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Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti U ovom radu ukratko je izložen pregled najznačajnijih metoda za procjenu jednoosne tlačne čvrstoće i Yangovog modula elastičnosti intaktnog stijenskog materijala koje su nastale u okviru mnogobrojnih istraživanja. Iznesen je prijedlog podjele metoda prema kojemu se one u osnovi mogu podijeliti na jednostavne i složene metode. Jednostavne metode uključuju različite dijagrame i tablice te primjenu jednadžbi jednostruke regresije, a složene metode uključuju primjene jednadžbi višestruke regresije, modela neizrazite logike, neuronskih mreža, evolucijskog programiranja i regresijskog stabla. Models for estimating uniaxial compressive strength and elastic modulusThe most significant methods for estimating the uniaxial compressive strength and Young's modulus of intact rock material, formulated in the scope of numerous previous studies, are briefly presented in the paper. The proposal for classification of these methods, according to which they can generally be divided into simple and complex methods, is also presented. Simple methods include various diagrams and tables and the use of simple regression equations, while complex methods comprise the use of multiple regression equations, fuzzy logic models, neural networks, evolutionary programming, and regression trees.

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A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock

Cited by 315 publications

References 28 publications

The use of neural networks for CPT-based liquefaction screening

The use of neural networks for CPT-based liquefaction screening

Using artificial neural network to predict dry density of soil from thermal conductivity

Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti

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