Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach

Abstract

Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key elements of effective maintenance optimization and prioritization. This paper proposes a novel risk-based framework that expands the potential of available data by considering the probabilistic susceptibility of assets in the prediction process. It combines a risk score generator with machine learning to forecast the hotspots of multiple defects while considering the interrelations between defects. With this, we developed a scalable algorithm, Multi-asset Defect Hotspot Predictor (MDHP), and then demonstrated its performance in a real-world case. In the case study, MDHP predicted the hotspots of three defects on paved ditches, considering the interrelation between paved ditches and five nearby assets. The results demonstrate an acceptable accuracy in predicting hotspots while highlighting the interrelation between adjacent assets and their contribution to future defects. Overall, this study offers a scalable approach with contribution in data-driven multi-asset maintenance planning with potential benefits to a broader range of linear infrastructures such as sewers, water networks, and railroads.