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Performance comparison of training algorithms for the estimation of b?hme abrasion resistance using neural networks

Journal of Mountain Science 2023年第12期20卷 3732-3742页

作者： Ali Can OZDEMIR Esma KAHRAMAN Department of Mining Engineering Çukurova UniversityAdana 01250Türkiye

Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the *** abrasion resistance of natural stones can be determined in the laboratory by applying the b?hme abrasion resistance(bAR)***,the direct analysis of bAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health *** eliminate these disadvantages,the estimation of bAR using artificial neural networks(ANN)was *** natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and bAR tests were carried *** outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and *** the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),bayesian regularization(bR),and scaled conjugate gradient(SCG)were compared for bAR prediction of natural *** criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive *** findings of the study indicated that ANN is a valid method for estimating the bAR ***,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the bR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction *** has been observed that the proposed method is quite practical to *** the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.

关键词： böhme abrasion resistance Neural networks Levenberg-Marquardt bayesian regularization Scaled conjugate gradient

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