A hybrid approach to predict vertical temperature gradient of ballastless track caused by solar radiation

The spatial and temporal temperature evolution of ballastless track is difficult to be determined, due to its complex thermal conditions. Accurate acquisition of vertical temperature gradient (VTG) of ballastless track is of great significance for its design, maintenance and damage control. However, few researchers have studied the predictive approach of spatial and temporal VTG evolution. In this study, a hybrid approach that combines experimental and finite element analysis with machine learning (ML) technique is proposed to automatically predict VTG of ballastless track induced by solar radiation. Six different ML techniques, i.e., multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGBOOST), and categorical boosting (CATBOOST) with fine-tuned hyperparameters are trained and validated by a dataset of 1000 cases retrieved from macroscale finite element analysis. The maximum and minimum air temperatures, latitude, average wind speed, etc., considering spatial and temporal environment actions are used as input variables to estimate the VTG of ballastless track. The results indicate that the CATBOOST-based hybrid approach demonstrates the most appropriate method for determining VTG of bal-lastless track among the above ML techniques. The proposed feasible and efficient hybrid approach is expected to be widely used for the identification of temperature evolution in structural engineering.

Automated summary

This study proposes a hybrid approach that combines experimental and finite element analysis with machine learning techniques to predict the vertical temperature gradient (VTG) of ballastless track induced by solar radiation. The results show that the CATBOOST-based hybrid approach is the most appropriate method for determining the VTG of ballastless track.