The Application of the Grey Neural Network in the Deflection Control of PC Rigid Frame Continuous Box Girder Bridges

Abstract

Deflection control is the crucial procedure in construction control of cantilever prestressed concrete continuous girder bridge. This paper summarizes the advantages of Grey theory’s poor information processing and abilities of Neural Network’s self-learning and adaption, and the combinational algorithm of grey Neural Network is applied to the prestressed concrete bridge cantilever construction control process. Firstly, GM (1, 1) model and BP artificial Neural Network algorithm to predict the elevation of construction process are introduced respectively. In addition, the elevation prediction model of rigid-framed-continuous girder bridge is established. By practicing in the construction control project of LongHua Bridge, the method is testified to be feasible. The results indicate that, the combinational algorithm of Gray Neural Network to predict the construction elevation has higher reliability and accuracy which can be an effective tool of construction control for the same type bridges.