CNAM Webinar - Data Analytics in Road Asset Management
Understanding the condition of roads is important to transportation departments and municipalities. Therefore, modeling and predicting the deterioration of roads is crucial. It is important to have a decision-support tool for predicting the condition of asphalt roads under a changing climate. Madeh Piryonesi has gathered and analyzed the data of several thousand road sections from the Long-Term Pavement Performance (LTPP) database in the hope of addressing these issues. The results of his works are implemented in a web-based platform, which includes a map with an interactive dashboard. Users can query any road, input its data, and get relevant predictions about its deterioration via machine learning algorithms. Since the attributes used for model training include important climatic stressors, such as temperature and precipitation, the users can run the model multiple times under different climate change scenarios and get pertinent analytics.
To show the functionality of the developed tool, two sets of examples were studied. First, the condition of four individual roads in different areas was predicted. Second, the data of two groups of roads, one in Ontario and one in Texas, was fed into the model under two different climates. The results are compared and explained. The findings of this study show that the climate change scenario affects different municipalities/communities differently. This type of analysis is highly important knowing that climate change will be a significant factor in future infrastructure decision-making. The results of such analyses could be useful to municipalities, policy-makers and transportation agencies who deal with large networks of assets under a varying climate.
