Fatigue damage prediction of carbon fiber reinforced polymer based on WOA-AdaBoost
In order to predict the fatigue damage extension of carbon fiber reinforced polymer more accurately,a fatigue damage prediction method for carbon fiber reinforced polymer was proposed based on WOA-AdaBoost.The time-domain waveform features,time-domain statistical features,and frequency-domain features in the sensing signals were extracted as inputs,and the damage area values as outputs,and AdaBoost integrated learning was introduced to predict their fatigue damage.In order to further improve the prediction accuracy,the whale optimization algorithm was used to optimize the learning rate and the number of weak learners in AdaBoost,and the prediction model of WOA-AdaBoost was constructed to achieve the fatigue damage prediction of carbon fiber reinforced polymer.The experimental results showed that the correlation coefficient of the established prediction method of WOA-AdaBoost was 0.949 and the values of its RMSE and MAE parameters were smaller compared to the prediction methods such as AdaBoost and SVM,the proposed method had a better prediction of the damage of carbon fiber reinforced polymer.
structural health monitoringcarbon fiber reinforced polymerfatigue damage predictionWOA-AdaBoost
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