Fitting and Prediction for Crack Propagation Rate Based on Machine Learning Optimal Algorithm

Establishing fatigue crack propagation rate is the key to forecasting structure fatigue lifetime, nine parameters fatigue crack propagation rate model and McEvily model are widely applied at present, but it is very complex to realize these models, partial derivative must be calculated and there is large deviation between fitted static parameter and actual value and physical conception isn´t clear. In accordance with the disadvantage above methods, Based on optimum parameter selection with grid search and cross validation, we presented optimal common machine learning algorithm (least squares support vector machine-LSSVM) method for fatigue crack propagation rate forecast. Complicated and strong nonlinear fatigue crack propagation rate curve was simulated by network design and conformation of LSSVM learning algorithm and the optimized SVM parameters were selected by the method of network searching and cross validation. Compared the errors with output value of the optimized model and output value from nine parameters fatigue crack propagation rate fitting model, LSSVM whose parameter was optimized with cross validation had excellent ability of nonlinear modeling and generalization. It provided a simple and feasible intelligent approach for material fatigue analysis.