Based on an amount of fatigue experimental data of fiber reinforced plastic composite, a new three-parameter S-N curve model is proposed to describe the relationships between the loads and fatigue life under constant amplitude cyclic loading. As the logistic curve behaves as sigmoidal which is the similar with previous S-N models, and from this comparability, an S-N equation with logistic’ form has been established. The model can assess the fatigue behaviors of FRP under various loading conditions, such as, tension-tension (T-T), tension-compression (T-C) or compression-compression (C-C) loading under different stress ratios of the whole region of fatigue life. Several examples are employed to illustrate that the model has ability to fit several different sets of experimental data accurately.

A dual-zone reinforcement ply stacking sequence optimization method used for comosite laminate with large cutout is present. The optimization method utilized a new Genetic Algorithm. The new Genetic Algorithm introduced a new strategy which can improve the efficiency of the traditional Genetic Algorithm and overcome the shortages of the worse convergency and prematurity of the Simple Genetic Algorithm. In the new Genetic Algorithm, the selection probability and the mutation probability are self-adaptive. Compared with the Simple Genetic Algorithm, the new Genetic Algorithm method shows good consistency, fast convergency and practical feasibility. By using the new Genetic Algorithm, the reinforcement ply stacking sequence optimization method got reasonable symmetric and balance stacking sequence which could meet the design requirements.

Fatigue damage growth can be described by the gradual reduction of the stiffness and strength, and damage expressed by the two degradation methods are equivalent. According this assumption, a pair of cumulative fatigue damage models based on residual stiffness and residual strength is proposed, and then the connection between the two damage indices is established. The two damage models follow equation with the same form, but the parameters in them are not equal. Each of the two models contains three unknown parameters which have pertinent effect on the damage growth rates, in addition, the models also take into account of the effects of stress levels and stress ratios. By fitting the experimental data, it is observed that the parameters in damage functions obey the linear relation well, and the unknown constants are deduced with finite tests easily. Finally, the models are found to give a good description of fatigue damage evolutions of different stacking sequence for both of the stiffness and strength.

Fatigue damage of composites can be described by the residual stiffness and residual strength, and the same damage state can be described by the two mechanical parameters equivalently. Based on this assumption, a new pair of fatigue damage accumulation models are established to simulate fatigue behavior and predict the fatigue life of composites. Each of two equations contains three parameters and has the similar form, and the power function relationships between the two damage indices are constructed. The proposed model, combining with constant life diagrams and failure criteria are used to estimate the fatigue life of composites, and good agreement is observed between the present model and experimental results.

This paper proposed a new method to detect the damage of composite skin/stringer panel structure using modal strain energy combined with neural network. The change ratio of element modal strain energy is choosen as damage indicator because of it’s highly sensitivity to the location and severity of structure damage. Neural network here play the role of a tool to indentity the damage according to the change ratio of modal strain energy. To achive this, a three layers neural network model is built and the BP arithmetic is used. The proposed method is validated using a numerical simulation of a composite skin/stringer panel with damages in some elements of its FEM mode, which are simulated by reducing elements’ material stiffness properties. The result shows that, this method is robust, accurate and highly efficient with the maximal error limited in 10%.