Shear Resistance of Reinforced Aerated Concrete Slabs: Prediction via Artificial Neural Networks

Year 2019, Volume: 4 Issue: 2, 344 - 350, 21.10.2019

Derya Bakbak Ahmet Emin Kurtoğlu

https://doi.org/10.29187/jscmt.2019.38

Cited By: 5

Abstract

Autoclaved aerated concrete (AAC) provides advantageous material characteristics such as high thermal
insulation and environmentally friendly properties. Besides its non-structural applications, AAC is being
considered as a structural material due to its characteristics such as lighter weight compared to normal concrete.
In this study, main focus is to test the usability of artificial neural networks (ANNs) in predicting the shear
resistance of reinforced AAC slabs. A large experimental database with 271 data points extracted from eleven
sources is used for ANN training and testing. Network training is accomplished via multi-layer backpropagation
algorithm. Based on random selection, the dataset is partitioned into two portions, 75% for network training and
25% is for testing the validity of the network. Different models with a varying number of hidden neurons are
developed to capture the network with optimum hidden neuron numbers. The results of each model are presented
in terms of correlation coefficient (R
2
) and mean squared error (MSE). Results suggest that the ANN model with
seven hidden neurons is the simplest model with most accurate predictions and ANNs can provide excellent
prediction ability with insignificant error rates.

Keywords

Artificial neural networks, autoclaved aerated concrete, , reinforced concrete slab, shear strength, modelling

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