Damage Diagnosis of Radial Gate Based on RBF Neural Networks

Damage diagnosis and health monitoring of large-scale structures are becoming a hot research subject in the present structural engineering circle. Aimed at many operating safe problems of hydraulic structure, a method applied to radial gate is put forward. This method is an aggregation of vibration theory, neural networks and pattern identification, and make the combined index as input data of RBF neural networks, and make the damaged locations and degree as output data. Based on the theory, a radial gate of a hydraulic project located in the middle reaches of the main stream of the Jialing River is studied. Study shows that this method has better function to get precise identified results, and this provides a new way to online state testing and monitoring for radial gate.