Transfer Learning for Pavement Performance Prediction

What is it about?

This paper proposes a transfer learning approach to develop pavement performance prediction models in limited data contexts. The proposed transfer learning approach is based on a boosting algorithm. In particular, a modified version of the popular TrAdaBoost learning algorithm was used.

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Why is it important?

The results of this work show that it is possible to develop accurate pavement performance prediction models in limited data contexts when a transfer learning approach is applied. The findings of this study should be of interest to road agencies facing limited data contexts and aiming to develop accurate prediction models that can improve their pavement management practice.