A BOA-LightGBM hybrid model for estimating the dynamic friction coefficient of asphalt pavements

The skid resistance of the pavement has a significant impact on road safety. To accurately evaluate the change mechanism of skid resistance, a hybrid model based on Bayesian optimization algorithm (BOA) and light gradient boosting machine (LightGBM) is proposed. Firstly, the electric sander is used to obtain the mean texture depth (MTD) of the asphalt pavement. Then, the friction coefficient of road surface under dry and wet conditions is measured by dynamic friction tester at corresponding positions. Finally, a BOA-LightGBM dynamic friction coefficient estimation model is proposed based on the mean texture depth, pavement water content, and vehicle speed. The BOA algorithm is used to adjust the optimal parameters of the model. Compared with the LightGBM model, the results show that the BOA-LightGBM model fits predicted and true values better through the above factors, and the R2 value is 97.21%. It proves that the proposed model has strong stability and nonlinear fitting ability.