Machine learning prediction of steel–concrete composite beam temperatures during hot asphalt paving

•Proposed a formula for predicting vertical temperature difference of steel–concrete composite beams during hot asphalt paving.•We explored a machine learning approach based on field measurements to predict the temperature field of steel–concrete composite beams during the hot asphalt paving process.•Using machine learning techniques, key features affecting the temperature field of steel–concrete composite beams during hot asphalt paving were identified, including beam temperature, hot asphalt temperature, ambient temperature, and box interior temperature. This study lays the foundation for subsequent research on the temperature field of steel–concrete composite beams under hot asphalt paving. In the bridge construction process, the temperature distribution within the steel–concrete composite beam (SCCB) under hot asphalt paving is not negligible in its impact on the structural performance. However, traditional static analysis methods for bridge temperature fields, such as temperature measurements and numerical simulations, are plagued by high workload and costly equipment requirements. Therefore, in this study, we explore a machine learning (ML) approach based on field measurements to predict the temperature field of SCCB during hot asphalt paving. The result showed that of the various ML algorithms tested, the K-Nearest Neighbors (KNN) algorithm provided the highest predictive accuracy for the temperature field of SCCB. Through feature selection and experimental analysis, we identify beam temperature (Tbt), hot asphalt temperature (Ts), ambient temperature (Ta), and box interior temperature (Tbox) as key features for predicting the temperature of SCCB during hot asphalt paving. This study demonstrates that ML is an powerful tool for predicting the thermal behavior of bridge structures, with potential widespread application in identifying temperature evolution in bridge structures.