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

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

References

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