Research on dam deformation monitoring model based on adaboost-SVM

Aiming of the complex non-linearity problem between environment and effect in concrete dam deformation prediction model and the low prediction accuracy of single support vector machine (SVM) model,an AdaBoost-SVM model for concrete dam deformation prediction was proposed.The model adopted the principle of minimizing structural risk and used the idea of learning algorithm for reference to improve the learning performance of the model for enhancing the generalization ability and prediction accuracy of the model.The prototype monitoring data of concrete dam displacement were trained and predicted by the AdaBoost-SVM prediction model,and the prediction results were compared with those of the single support vector machine model.The results showed that the mean square deviation of the prediction model based on AdaBoost-SVM was 0.5565, and the absolute average error was 0.40,while the prediction accuracy was one number higher than that of the single support vector machine model.In addition,compared with the single support vector machine prediction model,the enhanced model showed better stability in the prediction period.The model combined the advantages of lifting algorithm and support vector machine,and can be used as an effective method for deformation prediction of concrete dams.

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