Abstract:
In recent years, there has been an increasing interest in the application of machine learning for the
prediction of pavement performance. Prediction models are used to predict the future pavement
condition, helping to optimally allocate maintenance and rehabilitation funds. However, few studies
have proposed a systematic approach to the development of machine learning models for pavement
performance prediction. Most of the studies focus on artificial neural networks models that are trained
for high accuracy, disregarding other suitable machine learning algorithms and neglecting the
importance of models’ generalisation capability for Pavement Engineering applications. This paper
proposes a general machine learning approach for the development of pavement performance
prediction models in pavement management systems (PMS). The proposed approach supports
different machine learning algorithms and emphasizes generalisation performance. A case study for
prediction of International Roughness Index (IRI) for 5 and 10-years, using the Long-Term Pavement
Performance, is presented. The proposed models were based on a random forest algorithm, using
datasets comprising previous IRI measurements, structural, climatic, and traffic data.