Abstract
Presented herein is a study on the use of low-cost technology for the data collection and clasification on roadway pavement defects, by use of sensors from smartphones and from automobiles' on-board diagnostic (OBD-II) devices while vehicles are in movement. The smartphone-based data collection is complimented with artificial intelligence-based (AI) pattern recognition techniques for the classification of detected anomalies. The proposed system architecture and methodology utilize eleven metrics in the analysis, are checked against three types of roadway anomalies, and are validated against hundreds of roadway runs (relating to several thousands of data points) with an accuracy rate of over 90 percent.
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