A FRAMEWORK FOR MODELLING THE THERMAL RESPONSE OF CONCRETE PAVEMENTS TO CLIMATE CHANGE USING MACHINE LEARNING

Modelling the thermal response of concrete pavements to climate change is essential for understanding the potential impact of changing environmental conditions on pavement performance. Traditional methods for predicting pavement temperatures rely on simplified empirical equations, which may not accurately capture the complex interactions between climate factors and pavement behaviour. This framework for modelling the thermal response of concrete pavements to climate change using machine learning. The proposed framework leverages machine learning algorithms to develop predictive models that capture the intricate relationships between climate factors and pavement temperatures. Historical climate data, including air temperature, solar radiation, wind speed, and humidity, are used as input features to train the machine learning models. The trained models can then be employed to simulate future pavement temperatures under different climate change scenarios. By utilizing machine learning, the framework overcomes the limitations of traditional methods by incorporating non-linear relationships and complex interactions between climate variables. The models can capture both short-term and long-term effects of climate change, enabling a more accurate representation of pavement temperature variations. This information is crucial for assessing the potential impact of climate change on pavement performance, such as thermal cracking, rutting, and durability. The application of the proposed framework has significant implications for pavement design and maintenance. By understanding how climate change affects pavement temperatures, engineers and researchers can make informed decisions regarding material selection, thickness design, and maintenance strategies. This knowledge can lead to more resilient and sustainable pavement infrastructure that can withstand the challenges posed by climate change. Furthermore, the framework offers a proactive approach to climate change adaptation in the transportation sector. By incorporating climate change scenarios into pavement design and management practices, infrastructure owners can mitigate potential risks and optimize the allocation of resources. The framework for modelling the thermal response of concrete pavements to climate change using machine learning. By leveraging historical climate data and machine learning algorithms, the framework enables accurate predictions of pavement temperatures under different climate scenarios. The findings contribute to the advancement of pavement engineering practices and provide a valuable tool for assessing the impact of climate change on pavement performance.