A machine learning methodology to predict alerts and maintenance interventions in roads
Francisco J. Morales, Antonio Reyes, Noelia Cáceres, Luis Romero, Francisco G. Benítez, João Morgado, Emanuel Felipe Duarte
Abstract
This contribution is about predicting maintenance alerts in roads and selecting the most appropriate type of interventions recommended for preventing the occurrence of future failures. The objective is aligned with that covered by pavement maintenance decision support systems (PMDSS), though the methodology presented can be applied to other non-pavement road linear assets. The purpose is to summarise the main findings in the development of an approach based on testing the four most extended machine learning techniques (ML), namely Decision Trees (DT), K-Nearest Neighbourhood (KNN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN), using data from the historical inventory of inspections and maintenance interventions of a case study to illustrate the potential that such approach can offer to road maintenance managers. The correlation process embodies supervised and unsupervised training of models. The maintenance predictions are presented and compared over various segments corresponding to the real maintenance interventions conducted on an existing road network of a geographical zone.