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

摘要

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