Using measured flight loads in full V-N envelope during flight tests, an adaptive safety-predicting load model for life cycles was built based on the combination of an improved genetic algorithm, a linear regression and BP neural network. The above adaptive method was used to build the safety-predicting load model of certain wing which was validated in full V-N envelope. Moreover, the effects of the sample on the model accuracy were analyzed. The results showed that the maximum and average errors of the predicted bending-moment in full V-N envelope are 10.6% and 1.0%, and those of the predicted shear in full V-N envelope are 9.1% and 0.4%, respectively. These errors are lower than those from optimized-linear and piece-wise-linear methods, and the convergence is better than that from the neural network. With the sample varying from full, 1/2, 1/3,…, 1/10, the maximum errors of the bending-moment and the shear change from 10.6% to 19.6% and from 9.1% to 27.9%, respectively.
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