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Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data

America’s aging infrastructure systems are facing a significant challenge due to the limited renovation funding. The most cost-effective strategy to improve the overall conditions of America’s road infrastructure is through the Preventive Maintenance, i.e., a planned treatment to an existing roadway system and its appurtenances before deficiencies develop. However, making robust preventive maintenance decisions on a relatively large section of roads can be a nontrivial task, due to various factors that need to be considered in the calculation. Therefore, this study aims to test a framework that maps pavement surface conditions based on running vehicles’ vibration data (via sensors built in most smartphones), and optimizes the preventive maintenance plans based on the deterioration modeling of the road system (urban level). The technical objectives include: 1) Understand the statistical relationship between vehicle vibration (monitored by smartphone accelerator sensors) and pavement damages; 2) Investigate deterioration modeling of the road system with vehicle vibration data; and 3) Test algorithms that optimize preventive maintenance plans for the overall performance of the entire system in the long term. This study will advance our knowledge about preserving existing road infrastructure through calculated preventive maintenance. The findings will enable the development of an automatic and economic road condition evaluation method by monitoring the vibration patterns of the running vehicles on the road.