The Prediction Of Continuous Rigid-Frame Bridges' Bearing Capacity Based On Dynamical Test

Reliability assessment of the bridge is an urgent problem to be resolved presently in engineering field. Fortunately ,thanks to its great learning ability and non-linear massive simultaneous managing capacity, the neural network provides a model for determining the bearing capacity of bridges. This paper, based on the knowledge of the dynamical test and the mechanism of the neural network, proposes the neural network model on the prediction of bearing capacity.Firstly, this paper expands some parameters and methods of load carrying capacity. Secondly,summarized the general methods and notices in dynamical test. Then, it introduces the basic concept and fundamental principle of neural network, the relevant characteristics of BP network and the principles of the genetic algorithm. Finally, on the basis of previous work, the paper sets up the neural network model on the prediction of bearing capacity .The paper mainly focuses on the feasibility and spueirority of the neural netwok model in solving the problem. Based on the analysis of all factors concerning the bearing capacity, it esbtablishes the 3-level BP network with stresses as neuron of input level and warping as neuron of output level, and works out a relevant program, making use of the neural network toolbox of MATLAB R2007. Contrapose the disadvantages of BP network,the paper employs the self-adaption method to adjust the learning rate, provisional estimate to determine the number of neuron joints of hidden layer and using genetic algorithm to optimize the initial weight of BP network.In the end, the paper applies the neural network model, compares the result by using neural network with results of dynamical test to prove a favorable prediction effect.