Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling

Abstract

Limited research has been conducted on the application of data analytics to the prediction of the Pavement Condition Index (PCI) of asphalt roads. More importantly, studies comparing the prediction results of these algorithms with other important performance indicators such as the International Roughness Index (IRI) are rare. This paper aims to train machine learning algorithms to predict the PCI and IRI of asphalt pavement using the Long-Term Pavement Performance (LTPP) database. To this end, 30,274 IRI and 3,227 PCI records were queried and prepared to train the models. The first result of using such an unprecedentedly large training set was a higher accuracy level compared to previous works. For example, the highest cross-validation accuracy for predicting the IRI and PCI numeric values (i.e., R2) was 0.95 and 0.84, respectively, which was the result of a random forest regression algorithm. Classification algorithms were used as well. The accuracy of gradient-boosted trees, for instance, reached 88% and 82%, respectively, when predicting the IRI and the PCI. Even higher accuracy levels were achieved after the data were segmented into separate climatic zones, with dry-and-no-freeze region gaining the highest accuracy. Another finding of this research was that the initial IRI has a larger role in the prediction compared to initial PCI. This observation was confirmed by multiple methods including studying the importance factors of a gradient-boosted trees algorithm and relevant correlation matrices of the attributes. Another important finding about the type of performance indicator was that simpler algorithms, such as linear regression or decision tree, can achieve higher accuracy in predicting the IRI.