Automatic Prediction of Maintenance Intervention Types in Roads using Machine Learning and Historical Records

Abstract

A methodology to support and automate the prediction of maintenance intervention alerts in transport linear-asset infrastructures can greatly aid maintenance planning and management. This paper proposes a methodology combining the current and predicted conditions of the assets, and unit components of the infrastructure, with operational and historical maintenance data, to derive information about maintenance interventions needed to avoid later severe degradation. By means of data analytics and machine learning techniques, the proposed methodology generates a prioritized listing, ranked on severity levels, corresponding to the pre-alerts and alerts generated for all assets of the transport infrastructure. The methodology is applied and tested in a real case consisting of a road network with different section classes. The analysis of the results shows that the algorithms and tools developed have good predicting capabilities.