Climate change impact on infrastructure: A machine learning solution for predicting pavement condition index

Abstract

A decision-support tool was developed to predict the condition of asphalt roads in 2, 3, 5 and 6 years. The tool was developed based on analyzing a large dataset (more than 3000 road sections) extracted from the Long-Term Pavement Performance (LTPP) database. Several algorithms were examined: two decision trees, k-nearest neighbors (k-NN), naïve Bayes classifier, naïve Bayes coupled with kernel estimator, random forest and gradient boosted trees. The last three achieved the highest accuracy levels (above 90%). The attributes used were intentionally selected to be related to climate stressors (such as temperature ranges, perspiration and freeze–thaw cycles) or basic road attributes (such as age and functional class) to enable the models quantify the impact of climate change. A major caveat of this study is that some climate stressors such as storm frequency and severity were not included in the model as there was no data available about them in the LTPP dataset. With the proposed tool, the impacts of different climate scenarios can be examined by running the model with inputs that reflect the attributes of each scenario. To illustrate this, we examined the deterioration of two sets of roads: one from Ontario and one from Texas. Each set was examined in two climate scenarios. The analysis showed lower levels of deterioration for the Ontario roads and exacerbation of deterioration for the roads in Texas. It means that climate change may exacerbate or alleviate road deterioration depending on location. This type of analysis can be beneficial to the long-term policymaking in road infrastructure. For example, notwithstanding the impact of climate attributes that are not considered in this study, an Ontario policymaker should expect that with the same design standards and the same maintenance regimes, the service levels of roads will be enhanced.