Despite measures taken to reduce emission of greenhouse gases to the atmosphere, the processes initiated by climate change and their negative consequences cannot be stopped immediately. In the Arctic and sub-Arctic areas, roads are particularly vulnerable and exposed to changes in winter weather conditions caused by climate change. For example, thin snow cover and rapid temperature decrease may cause massive fracturing in the shallow subsurface (frost quakes) and hence mechanical damage to the pavements and roads. The key tool to decrease economic losses due to this damage is to react before the strength and stability of roads and pavements weakens. In our project we shall develop a methodology for monitoring and prediction of seasonal changes of road conditions that uses physics-guided machine learning and artificial intelligence. The results can be used as a tool for proactive maintenance that would give longer lifetimes, savings and better roads and pavements in the long run.

2022

2024