Cable-stayed bridge cable damage positioning method based on modified reverse transmittance nerve network

Abstract

A cable damage localizing method for a cable-stayed bridge on the basis of improved back-propagation neural network is disclosed. The invention has the method that a BP neural network can be improved effectively based on how to effectively perform, thus a cable damage localizing of a cable-stayed bridge is carried out, and therefore, the cable damage localizing method for the cable-stayed bridge with the improvement is established. The invention proposes to improve traditional BP algorithm jointly with Bold Driver technology, momentum item increase, simulated annealing algorithm and random hill-climbing algorithm. The cable damage localizing method has the advantages of accelerating convergence, improving learning-efficiency improvement and preventing the learning process from falling into local minimum and, in addition, the invention can reasonably determine network topologies.

Description

technical field [0001] The invention is a method applied to structure damage location, especially a method applied to cable-stay damage location of cable-stayed bridges. Background technique [0002] BP (Back Propagation, backpropagation) neural network technology has been applied to structural damage identification and related research since the 1990s. Wu et al. [1] A numerical simulation of damage identification is carried out on a three-layer frame structure using a single hidden layer BP network. Elkordy et al. [2] The damage diagnosis of a 5-story steel frame is carried out by using BP network. Hanagud and Luo [3] Damage identification of composite panels using frequency response function data based on BP neural network; Luo and Hanagud [4] In the subsequent research, the dynamic learning rate was introduced into the above research to speed up the convergence of the neural network. For glass fiber composite beams, Jenq and Lee used the change of the first 4 order ...

AI Technical Summary

Problems solved by technology

However, the current research work generally has deficiencies in this aspect, especially for the analysis of damage characteristics of long-span cable-supported bridge structures.

[0008] 2) BP neural network is the most widely used neural network in engineering. However, BP network itself has some inherent defects, such as slow learning convergence, easy to fall into local minimum, and difficult to determine the network structure

In the application of structural damage recognition, some researchers have paid attention to the problem of slow learning convergence of BP neural network, and conside

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