Structural Damage Detection Based On Artificial Neural Network

Structural damage detection based on the measured modal data is the prevalent and difficult issue at present. A structural damage detection method based on modified Back-Propagation neural network(BPNN) is presented in this thesis. Owing to the advantages of BP neural network (traditional BP neural network), i.e., non-linear, tolerance and robust, it has played a very important role in structural damage detection. However, Because of the limitation of itself, traditional BP neural network encounters two main problems in practice: (1) lack of systematic means in designing network's structure and original parameters; (2) convergence to a local minimum.In this thesis, an improved BP algorithms based on genetic algorithm(GA)-BP neural network combined technology has been proposed to solve the two problems mentioned above. The genetic algorithm coding in the real number is engaged to optimize the structure and original parameters of BP neural network, so the network can learn the training patterns more accurately. Three numerical simulations have showed that the GABP has a better stability, precision and robustness than the traditional BP neural network, and is the reliable and accurate methods in structural damage detection.