Abstract
Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function. In this paper, the finite element model of a through tied arch bridge is modified based on Neural Network. The approximation function of RBF neural network is utilized to fit the implicit function relationship between the response of the bridge and its design parameters. Then the finite element model of the bridge structure is modified. The results show that RBF neural network is efficient to modify the model of a through tied arch bridge.
Highlights
The longitudinal stiffening girder of bridge main span is a single box and five chambers bidirectional prestressed concrete box structure, solid section beam at end
A radial basis function (RBF) neural network is determined by determining the center cj and width σj of the neuron basis function in the hidden layer and the weight wj from the hidden layer to the output layer [3]
It can be seen from the figure that the training of neural network based on MATLAB
Summary
The longitudinal stiffening girder of bridge main span is a single box and five chambers bidirectional prestressed concrete box structure, solid section beam at end. The arch rib is a steel box structure, the transition between arch rib and main girder joints with steel-concrete joints, which is 6.32m long. The vector height is 55.5 meters, and the rise-span ratio is 1/4.5. There are 33 pairs of suspenders on the whole bridge. The distance between the bridge suspender is 7 meters, and the transverse spacing of the bridge is 3.8 meters
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
