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Structural Engineering and Mechanics
  Volume 46, Number 6, June25 2013, pages 853-868

Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams
Mohammad Mohammadhassani, Hossein Nezamabadi-pour, Meldi Suhatril and Mahdi Shariati

    The comparison of the effectiveness of artificial neural network (ANN) and linear regression (LR) in the prediction of strain in tie section using experimental data from eight high-strength-self-compactconcrete (HSSCC) deep beams are presented here. Prior to the aforementioned, a suitable ANN architecture was identified. The format of the network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of eleven and ten neurons in first and second TRAINLM training function was highly accurate and generated more precise tie strain diagrams compared to classical LR. The ANN\'s MSE values are 90 times smaller than the LR\'s. The correlation coefficient value from ANN is 0.9995 which is indicative of a high level of confidence.
Key Words
    strain; deep beams; artificial neural network; STM; linear regression
Mohammad Mohammadhassani, Meldi Suhatril and Mahdi Shariati : Department of Civil Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
Hossein Nezamabadi-pour : Department of Electrical Engineering, Shahid Bahonar University of Kerman, Iran

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