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Computers and Concrete Volume 12, Number 2, August 2013 , pages 187-209 DOI: https://doi.org/10.12989/cac.2013.12.2.187 |
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Prediction of the transfer length of prestressing strands with neural networks |
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José R. Martí-Vargas, Francesc J. Ferri and Víctor Yepes
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| Abstract | ||
| This paper presents a study on the prediction of transfer length of 13 mm seven-wire prestressing steel strand in pretensioned prestressed concrete members with rectangular cross-section including several material properties and design and manufacture parameters. To this end, a carefully selected database consisting of 207 different cases coming from 18 different sources spanning a variety of practical transfer length prediction situations was compiled. 16 single input features and 5 combined input features are analyzed. A widely used feedforward neural regression model was considered as a representative of several machine learning methods that have already been used in the engineering field. Classical multiple linear regression was also considered in order to comparatively assess performance and robustness in this context. The results show that the implemented model has good prediction and generalization capacity when it is used on large input data sets of practical interest from the engineering point of view. In particular, a neural model is proposed -using only 4 hidden units and 10 input variables- which significantly reduces in 30% and 60% the errors in transfer length prediction when using standard linear regression or fixed formulas, respectively. | ||
| Key Words | ||
| transfer length; prestressing strand; prestressed concrete; neural networks; machine learning | ||
| Address | ||
| José R. Martí-Vargas and Víctor Yepes: Department of Construction Engineering, Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain Francesc J. Ferri: Department of Computer Science, Universitat de València, Av. Universitats s/n, 46100 Burjassot, Spain | ||