The Application of BP Neural Networks in Cable-Stayed Construction Control

Abstract:

Through the related technical information of the monitoring of bridge construction, this is to determine BP neural network's input and output parameters, to establish the neural network fore-casted model, and to carry through the prediction to the main beam of deflection. In the application, the data of deflection deviation has generated through the cast-in-place segmental cantilever to train the BP neural network. The deviations which are produced in the pre-run neural networks predict the follow-up segment in construction, and then simulation control in the cable-stayed bridge is realized in construction process.

• Periodical: Applied Mechanics and Materials (Volumes 584-586)
• Pages: 2001-2005
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.584-586.2001
• Online since: July 2014
• Authors: Xi Fen Zhang
• Keywords: BP Neural Network (BPNN), Bridge Construction Control, Cable-Stayed, Prediction

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