Prediction of the transfer length of prestressing strands with neural networks

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.Funding for this study were received from the Spanish Ministry of Science and Innovation and ERDF (Research Project BIA2006-05521, BIA2009-12722, and BIA2011-23602) and from the Spanish Ministry of Education (TIN2009-14205-C04-03 and Consolider Ingenio 2010 CSD2007-00018), as well as the European Community with the FEDER funds.Martí Vargas, JR.; Ferri Rabasa, FJ.; Yepes, V. (2013). Prediction of the transfer length of prestressing strands with neural networks. Computers and Concrete. 12(2):187-209. https://doi.org/10.12989/cac.2013.12.2.18718720912