Prediction of dynamic deformation modulus of subgrade based on GA-XGBoost model

In order to improve the real-time detection and evaluation accuracy of intelligent compaction (IC) quality, a continuous compaction quality prediction method based on GA-XGBoost model was proposed to improve the prediction accuracy of dynamic deformation modulus (E_vd). The model takes the dynamic deformation modulus as the goal, establishes a machine learning model, mainly uses the decision tree algorithm, and constructs the XGBoost model to predict and analyze the compaction quality. In order to improve the prediction accuracy and reliability of the model, genetic algorithm (GA) is introduced to optimize the hyperparameters of the model. Firstly, through the field engineering test, the vibration acceleration of the roller is measured, the acceleration signal is analyzed, the signal statistics are calculated and the harmonic frequency is obtained by fast Fourier transform (FFT), and the system connection between the characteristic factors and E_vd is preliminarily established. Secondly, the characteristics of each time-frequency domain are screened, the correlation analysis is carried out, and the characteristics with high correlation are selected to establish the prediction model. Finally, it is verified that the GA-XGBoost prediction model can better predict E_vd. The results show that the genetic algorithm (GA) can efficiently determine the hyperparameters of the XGBoost algorithm, and it shows better convergence speed than the single XGBoost model. By optimizing the feature factors and changing the input parameters, the prediction accuracy of the GA-XGBoost model is improved. The optimized mean square error is 3. 9% and the correlation coefficient is 0. 748. At the same time, compared with the traditional CMV fitting E_vd method, the machine learning model can greatly improve the prediction accuracy.