Using Artificial Neural Network (ANN) for Prediction of Climate Change Impacts on Jointed Plain Concrete Pavement

Driven by human influence, Canada's climate has warmed and will warm further at a rate of double the global average. Climate change phenomenon, commonly known as global warming has caused and will continue to cause irreversible temperature rise as well as other environmental anomalies that will affect transportation infrastructures. Hence, with continued growth in greenhouse gas (GHG) emissions in future, rising temperatures will have consequences on the short and long-term performance of the Jointed Plain Concrete Pavement (JPCP) systems. In this study, climate change impact on a typical JPCP structure was modeled using Pavement ME Design (PMED) software. The PMED modeling results were fed into a two-layer feed-forward network with sigmoid hidden neurons and linear output neurons. Results of this study indicated that the developed ANN models are effective and capable of accurately predicting the potential and relative impact of climate change on JPCP.


Shafiee, Mohammad
Maadani, Omran
Fahiem, Eslam

Session title

Innovations in Pavement Management, Engineering and Technologies









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