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Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings

AI in Transportation

Improved Prediction of Soil Thermal Properties using Recurrent Neural Networks

Monday, June 17, 2024
1:30 PM - 2:45 PM ET
Room: Galleria
Abstract:

Frost effects, such as frost heave and thaw weakening, can significantly degrade pavements. Effective mitigation relies on accurate prediction of soil thermal properties. Current prediction methods use empirical equations or finite element analysis, with recent progress using machine learning. One potentially appropriate machine learning model may be a recurrent neural network, which takes in present data as input, and outputs an estimation of future data, which can then be fed back into the model recursively to make further predictions. Using this method and training data from Hill Air Force Base, Utah and Air Force Academy, Colorado, we were able to forecast soil parameters including temperature, thermal conductivity, and moisture content for frost susceptible soils, with a deviation from experimental values of no more than 5.3 ℉ for a seven-day forecast, with the most significant contributions to accuracy being the use of Gated Recurrent Unit neurons and the incorporation of thermal conductivity.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • better understand the benefits of machine learning in prediction of soil thermal properties
  • understand the importance of providing thermal conductivity data as input to a machine learning model
  • understand the importance of recurrent machine learning models and their benefits for general properties forecasting.