01769597

Component

https://doi.org/10.1177/03611981211006105

The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone technology for the spatial condition-assessment mapping of roadway pavement surface anomalies. The study explores the use of data, collected by sensors from a smartphone and a vehicle’s onboard diagnostic device while the vehicle is in movement, for the detection of roadway anomalies. The research proposes a low-cost and automated method to obtain up-to-date information on roadway pavement surface anomalies with the use of smartphone technology, artificial neural networks, robust regression analysis, and supervised machine learning algorithms for multiclass problems. The technology for the suggested system is readily available and accurate and can be utilized in pavement monitoring systems and geographical information system applications. Further, the proposed methodology has been field-tested, exhibiting accuracy levels higher than 90%, and it is currently expanded to include larger datasets and a bigger number of common roadway pavement surface defect types. The proposed system is of practical importance since it provides continuous information on roadway pavement surface conditions, which can be valuable for pavement engineers and public safety.

© National Academy of Sciences: Transportation Research Board 2021.

English

pp 1118-1126

2021-9

Transportation Research Record: Journal of the Transportation Research Board

Web

References (15)

Algorithms; Condition surveys; Machine learning; Mapping; Neural networks; Pavement maintenance; Pavement management systems; Regression analysis; Smartphones; Surface course irregularities

Data and Information Technology; Highways; Maintenance and Preservation; Pavements

TRIS, TRB, ATRI

Apr 10 2021 3:10PM