Impact of Meteorological Factors on Asphalt Pavement Surface Temperatures: A Machine Learning Approach
Journal Title: Journal of Civil and Hydraulic Engineering - Year 2024, Vol 2, Issue 2
Abstract
Recent observations of global warming phenomena have necessitated the evaluation of the service performance of asphalt pavements, which is substantially influenced by surface temperature levels. This study employed twelve distinct machine learning algorithms—K-neighbors, linear regression, multi-layer perceptron, lasso, ridge, support vector regression, decision tree, AdaBoost, random forest, extra tree, gradient boosting, and XGBoost—to predict the surface temperature of asphalt pavements. Data were sourced from the Road Weather Information System of Iowa State University, comprising 12,581 data points including air temperature, dew point temperature, wind speed, wind direction, wind gust, and pavement sensor temperature. These data were segmented into training (80%) and testing (20%) datasets. Analysis of model outcomes indicated that the Extra Tree algorithm was superior, exhibiting the highest R2 value of 0.95, whereas the Support Vector Regression algorithm recorded the lowest, with an R2 value of 0.70. Furthermore, Shapley Additive Explanations were utilized to interpret model results, providing insights into the contributions of various predictors to model outcomes. The findings affirm that machine learning algorithms are effective for predicting asphalt pavement surface temperatures, thereby supporting pavement management systems in adapting to changing environmental conditions.
Authors and Affiliations
Tahsin Baykal, Fatih Ergezer, Ekinhan Eriskin, Serdal Terzi
Keywords
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- EP ID EP738918
- DOI https://doi.org/10.56578/jche020203
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