Prediction of Defect Hotspots for Highway Maintenance Management: A Multi-asset Machine Learning Approach

Given multiple budget and revenue constraints that the transportation sector encounters, predictive analytics enables maintenance agencies to make effective decisions, prioritize maintenance tasks, and provide efficient life-cycle planning. To this end, risk-based predictive models have provided promising results in representing the susceptibility of assets to future defects. Hence, the main objective of this study is to provide an integrated framework for predicting the occurrence probability of multiple defects on different highway asset types. Several gaps in previous models were identified, including limitations in predictive frameworks given the inadequate scope of available inspection data, expert-based selection of contributing factors, and ignoring the interrelationships between neighboring assets. Therefore, this study proposes a risk-based method that combines a risk score generator and a Machine Learning (ML) algorithm to predict the hotspots of multiple defects in a given roadway. To find the best fit, the model is chosen from a pool of ML algorithms selected from different categories. To measure the efficiency of the proposed model, its performance is investigated on a selected case study. The proposed framework produced significant accurate results within the extent of available data in the case study for calculating risk scores of erosion, obstruction, and cracking on paved ditches given historical weather, traffic, maintenance, and inspection data of five selected neighboring assets (flexible pavements, unpaved ditches, slopes, small pipes and box culverts, and under drain pipes and edge drains). Additionally, the contribution of the considered factors was investigated to further study the importance of individual contributors. The framework offers decision-makers a holistic view of degradation risks of multiple assets, which could enable them to prepare an integrated asset management program. Additionally, a similar framework can be applied to other linear infrastructure systems such as sanitary sewers, water networks, and railroads.