Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams

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 archit...

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Bibliographic Details
Published in:	Structural engineering and mechanics Vol. 46; no. 6; pp. 853 - 868
Main Authors:	Mohammadhassani, M, Nezamabadi-Pour, H, Suhatnil, M, Shariati, M
Format:	Journal Article
Language:	English
Published:	국제구조공학회 25-06-2013
Subjects:	Architecture Beams (structural) Confidence intervals Learning theory Neural networks Neurons Regression Strain 토목공학
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 G704-000405.2013.46.6.001
ISSN:	1225-4568
DOI:	10.12989/sem.2013.46.6.853