Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods
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Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Hindawi Limited
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate. © 2019 M. Timur Cihan.
Açıklama
Anahtar Kelimeler
Kaynak
Advances in Civil Engineering
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
2019
