Utilizing artificial neural networks to predict the asphalt pavement profile temperature in western Europe

The temperature profile significantly influences the structural performance of asphalt pavement since it influences various mechanical parameters such as stiffness, strength, and fatigue life. The present study aims to achieve two main objectives. Firstly, to explore the potential of Machine Learning (ML) approaches in predicting asphalt pavement profile temperature in the western Europe climate. Secondly, to determine the impact of different weather parameters on the effectiveness of the prediction models. Therefore, three ML algorithms are used to develop asphalt temperature prediction models: autoencoder network, Feedforward Neural Network (FFNN), and Long Short-Term Memory (LSTM).