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http://dx.doi.org/10.12989/cac.2013.11.3.237

Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams  

Mohammadhassani, Mohammad (Department of Civil Engineering, University of Malaya)
Nezamabadi-pour, Hossein (Department of Electrical Engineering, Shahid Bahonar University of Kerman-Iran)
Jumaat, Mohd Zamin (Department of Civil Engineering, University of Malaya)
Jameel, Mohammed (Department of Civil Engineering, University of Malaya)
Arumugam, Arul M.S. (Department of Civil Engineering, University of Malaya)
Publication Information
Computers and Concrete / v.11, no.3, 2013 , pp. 237-252 More about this Journal
Abstract
This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.
Keywords
deflection; deep beams; artificial neural network; high strength self compacting concrete; linear regression;
Citations & Related Records
Times Cited By KSCI : 14  (Citation Analysis)
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