ÇAKIROĞLU, Melda Alkan Süleyman Demirel Üniversitesi - Teknik Eğitim Fakültesi - Yapı Eğitimi Bölümü, Turkey , ÇİMEN, Ozan Süleyman Demirel Üniversitesi - Teknik Eğitim Fakültesi - Yapı Eğitimi Bölümü, Turkey

Prediction of Tensile Strength of Paving Stone Produced by Rebound Materials Using Artificial Neural Network Method

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In today numerical methods developing parallel with the development of computer technology widely used in the estimation of test results. One these methods Artificial Neural Networks (ANN), which is a sub-branch of artificial intelligence method. In this study, tensile strength values of paving stone produced by rebound material was developed a model to predict using ANN. In study, 29 unit paving stone samples were produced using the rebound material during shotcrete applications in order to determine the tensile strength of the paving stone. On the produced paving stone samples were made abrasion resistance tests. The performance of the developed ANN model to predict the values of tensile strength was evaluated by the correlation coefficient and average absolute error values. As a result, when evaluating the performance of the ANN model developed, has been shown usable in the tensile strength of the paving stones of ANN approach.

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Dry Mix Shotcrete , Paving Stone , Tensile Strength , Artificial Neural Networks.

Journal Of Natural an‎d Applied Sciences

65