Neural networks for prediction of deflection in composite bridges

Autor:	K. A. Patel, Zekarias Tadesse, Sandeep Chaudhary, A. K. Nagpal
Rok vydání:	2012
Předmět:	Engineering Artificial neural network business.industry Finite element software Lag Composite number Metals and Alloys Building and Construction Structural engineering Finite element method Cracking Mechanics of Materials Deflection (engineering) business Civil and Structural Engineering Test data
Zdroj:	Journal of Constructional Steel Research. 68:138-149
ISSN:	0143-974X
DOI:	10.1016/j.jcsr.2011.08.003
Popis:	Neural networks have been developed for prediction of deflections, at service load, in steel-concrete composite bridges incorporating flexibility of shear connectors, shear lag effect and cracking in concrete slabs. Three neural networks have been presented to cover simply supported bridges, two span continuous bridges and three span continuous bridges. The use of the neural networks requires a computational effort almost equal to that required for the simple beam analysis (neglecting flexibility of shear connectors, shear lag effect and cracking of concrete). The training and testing data for neural networks are generated using finite element software ABAQUS. The neural networks have been validated for number of bridges and the errors are found to be small. Closed form solutions are also proposed based on the developed neural networks. The networks/ closed form solutions can be used for rapid prediction of deflection for everyday design.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e70dcffdf8189e148a6f533ddf265baf https://doi.org/10.1016/j.jcsr.2011.08.003 Zobrazit plný text záznamu Full Text from ScienceDirect