Abstract
Road pavement damage inspection is a critical yet challenging task. At present, road pavement damage inspection is usually done by DOTs using a manual process. Another emerging method of inspection is via the use of sensors, such as the use of LiDAR. This study proposes an automated road damage recognition method via the Sparse Coding analysis of vehicle vibrations. Sparse Coding is a class of unsupervised methods that learn data patterns based on extracted overcomplete bases. Unlike frequency domain-based analysis, e.g. Spectral Analysis, Sparse Coding analysis preserves the temporal information of the vehicle vibration that contains important patterns related to road pavement damage. A preliminary study was performed with vehicle vibration data collected in College Station, Texas. Results confirm the feasibility of the proposed method in automated road pavement damage recognition. More data points should be collected in the future to further benchmark the effectiveness of the proposed method.
Highlights
The most cost-effective strategy to improve the overall condition 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 [1]
In this study we proposed an automated road pavement damage recognition method based on the vibration patterns of moving vehicles
To improve the effectiveness of using low quality data, we proposed the use of Sparse Coding, a deep learning method that finds a sparse representation of the input raw data in the form of a linear combination of basic elements called bases or atoms
Summary
The most cost-effective strategy to improve the overall condition 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 [1]. Decision makers must be able to identify the critical sections, predict the temporal deterioration of each and every section of the roads, and distribute limited resources in a holistic way to optimize the long-term performance of the entire system, instead of local sections. It requires large amounts of high-quality live data about the pavement surface conditions. This study aims to address the technical difficulty of collecting high fidelity data of pavement surface condition by leveraging existing technologies and hardware To overcome this challenge, this paper proposes an automated road damage recognition method based on the Sparse Coding of moving vehicles’ vibration data. The reminder of this paper will introduce the technical background and a preliminary study
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