Research On Application Of Self-adaptive BP Neural Networks To The Detection Of Delaminated Damage In Composite

Structural health monitoring system is an important research direction in the hot area of smart material and structures and Composite damage detection is an important research content of structural health monitoring.A compressive discussion about neural network and its improved algorithm were presented in this paper, using node law to confirm composite's grand elasticity and consistency, putting forward same coordinate but different node mark to simulate delamination. This study adopts active detection technique based on Lamb Wave to monitor damage in composite materials. The waves emitted interact with discontinuities and experience a change in their propagation characteristics when damage is generated. By comparing sensor signals collected before and after damage happens, damage location can be determined.Two carbon fiber reinforced composite beams were fabricated , and their model frequencies were measured by an experiment method. A novel method combining computational mechanics and neural network was demonstrated for composite health monitoring; The first five flexure model frequencies obtained by FEM were modified by a primary revising approach and were used to train the neural network. A program of self-adaptive BP neural network using MATLAB was finished and the first five flexure experimental model frequencies were input to the neural network to predict the delamination location.