Predicting the flexural behaviour of reinforced concrete and lightweight concrete beams by ANN

In this study, artificial neural network (ANN) method is used to predict the deflection values of beams and compared with the experimental results of a testing series. For this purpose six reinforced concrete beams with constant rectangular cross-section are prepared and tested under pure bending. The concrete of the test specimens is casted using the lightweight aggregates obtained from volcanic sediments. The lightweight concrete has some advantages comparing the traditional concrete, such as less self weight, less earthquake forces due to decreased mass, good sound and thermal insulation. The use of lightweight concrete in the construction industry is popular due to various advantages. The neural network procedure is applied to determine or predict the deflection values of 1/1 scaled model beams. The analytical results are compared with the test results and further predictions, including different mix designs can be possible at the end of the study. As a result, while the statistical values RMSE, R 2 and MAE from training in ANN model are found as 0.266, 99.2% and 0.216, respectively, these values are found in testing as 0.370, 96.47% and 0.419, respectively.