Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models

Yilmaz, Işık; Yuksek, Gürkan

doi:10.1016/j.ijrmms.2008.09.002

Cited by 279 publications

(67 citation statements)

References 29 publications

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“…Furthermore, the low accuracy of the MLR approach in estimating the measured SSSS and GMD values might be associated with the sample distribution, spatial variation, and the study area scale effects. The major conceptual limitation of all regression techniques that is, one can only ascertain relationships but never be sure about underlying causal mechanism, should be also considered (Yilmaz and Yuksek, 2009). …”

Section: Resultsmentioning

confidence: 99%

Prediction of soil physical properties by optimized support vector machines

Besalatpour¹,

Hajabbasi²,

Ayoubi³

et al. 2012

International Agrophysics

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A b s t r a c t. The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods.K e y w o r d s: soft computing, support vector machines, simulated annealing algorithm, soil shear strength, aggregate stability

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Section: Resultsmentioning

confidence: 99%

Prediction of soil physical properties by optimized support vector machines

Besalatpour¹,

Hajabbasi²,

Ayoubi³

et al. 2012

International Agrophysics

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“…Adaptive NeuroFuzzy Inference Systems) unutar programskih paketa Matlab Version 7,1 i SPSS 10,0. Na osnovi usporedbe rezultata došli su do zaključka da u procjeni UCS i E za stijene gipsa najbolje rezultate postiže neizrazita vrsta neuronskih mreža ANFIS, zatim model s unaprjeđenom vrstom neuronskih mreža, a najslabije rezultate postiže višeregresijski model [33]. Heidari i suradnici opisali su upotrebu neuronskih mreža MLP i RBF arhitekture u procjeni E za vapnence, dolomite i lapore s područja Lorestan u Iranu.…”

Section: Procjena Pomoću Neuronskih Mrežaunclassified

“…Manouchehrian je sa suradnicima izradio model na bazi umjetnih neuronskih mreža za procjenu jednoosne tlačne čvrstoće pješčenjaka pomoću petrografskih karakteristika [35]. U publikacijama [29,[32][33][34][35][36]46] uspoređeni su modeli višestruke regresije i neuronskih mreža te je zaključeno da neuronske mreže daju bolje rezultate procjena. Usporedba se obavljala na temelju koeficijenta korelacije između izmjerenih i procijenjenih veličina UCS i E te veličine korijena srednje kvadratne pogreške (engl.…”

Section: Procjena Pomoću Neuronskih Mrežaunclassified

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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“…Another reason for the low accuracy of the MLR approach in estimating the measured SSSS values might be associated with the sample distribution, spatial variation, and study area scale effects. Furthermore, the major conceptual limitation of all regression techniques, that one can only ascertain relationships, but never be sure about the underlying causal mechanism, should be considered (Yilmaz and Yuksek 2009). Sobhani et al (2010) also reported that the regression models may have low accuracy and prediction capability.…”

Section: Regression Modelmentioning

confidence: 99%

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

Besalatpour

Hajabbasi

Ayoubi

et al. 2012

Soil Science and Plant Nutrition

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Surface soil shear strength can be a useful dynamic index for soil erodibility and thus a measure of soil resistance to water erosion. In this study, we evaluated the predictive capabilities of artificial neural networks (ANNs) and an adaptive neuro-fuzzy inference system (ANFIS) in estimating soil shear strength from measured particle size distribution (clay and fine sand), calcium carbonate equivalent (CCE), soil organic matter (SOM), and normalized difference vegetation index (NDVI). The results showed that the ANN model was more feasible in predicting the soil shear strength than the ANFIS model. The root mean square error (RMSE), mean estimation error (MEE), and correlation coefficient (R) between the measured soil shear strength and the estimated values using the ANN model were 0.05, 0.01, and 0.86, respectively. In ANFIS analysis, the RMSE was 0.08 and a lower correlation coefficient of 0.60 was obtained in comparison with the ANN model. Furthermore, the ANN and ANFIS models were more accurate in predicting the soil shear strength than was the conventional regression model. Results indicate that the ANN model might be superior in determining the relationships between index properties and soil shear strength.

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Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models

Cited by 279 publications

References 29 publications

Prediction of soil physical properties by optimized support vector machines

Prediction of soil physical properties by optimized support vector machines

Modeli za procjenu jednoosne tlačne čvrstoće i modula elastičnosti

Soil shear strength prediction using intelligent systems: artificial neural networks and an adaptive neuro-fuzzy inference system

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