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Computers and Concrete
  Volume 3, Number 5, October 2006, pages 335-355
DOI: http://dx.doi.org/10.12989/cac.2006.3.5.335
 


Using radial basis function neural networks to model torsional strength of reinforced concrete beams
Chao-Wei Tang

 
Abstract
    The application of radial basis function neural networks (RBFN) to predict the ultimate torsional strength of reinforced concrete (RC) beams is explored in this study. A database on torsional failure of RC beams with rectangular section subjected to pure torsion was retrieved from past experiments in the literature; several RBFN models are sequentially built, trained and tested. Then the ultimate torsional strength of each beam is determined from the developed RBFN models. In addition, the predictions of the RBFN models are also compared with those obtained using the ACI 318 Code equations. The study shows that the RBFN models give reasonable predictions of the ultimate torsional strength of RC beams. Moreover, the results also show that the RBFN models provide better accuracy than the existing ACI 318 equations for torsion, both in terms of root-mean-square error and coefficients of determination.
 
Key Words
    reinforced concrete beam; torsional strength; radial basis function network.
 
Address
Department of Civil Engineering, Cheng-Shiu University,rnNo. 840, Chengcing Road, Niaosong Township, Kaohsiung County, Taiwan, R.O.C.
 

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