Research on prediction method of cable tower pre-bias based on model fusion

Traditional construction monitoring is labor-intensive and faces the problems of lengthy calculation cycles and high experience requirements. Oriented by data-driven, a method for pre-bias prediction of cable towers based on model fusion is proposed. First, collect and build an experimental data set containing 125 samples. After data preprocessing, establish SVR and KNN models with initial parameters. And conduct model evaluation after cross-validation and hyperparameter optimization. Second, utilize the mean absolute error (MAE) to define the loss function of the fusion model, and obtain the fusion weight of the weak learner. The weighted average method is used to fuse the SVR and KNN models. The deep integration of SVR and KNN is realized at the model decision level, which further improves the model fitting performance and the reliability of the prediction results. Finally, the performance of each model is evaluated based on the proposed comprehensive evaluation index. The experimental results demonstrate that the SVR and KNN models perform well on tasks with small sample sizes, and the fusion model has an MAE of 1.361, MAPE of 0.149, RMSE of 2.08, and 2 R of 0.997 on the test set. The research method and content have greatly improved the precision and efficiency of the pre-bias control of the tower. Which can be used to improve and extend the existing finite element analysis method.