Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment

2018 ◽  
pp. 1-22
Author(s):  
Amir H. Alavi ◽  
William G. Buttlar
Keyword(s):  
Machine Learning ◽  
Condition Assessment ◽  
Pavement Condition ◽  
Airport Pavement ◽  
Smartphone Technology ◽  
Pavement Condition Assessment

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

2021 ◽  
pp. 036119812110061
Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Condition Assessment ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geographical Information ◽  
Related Field ◽  
Pavement Surface ◽  
Automated Method ◽  
Smartphone Technology

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.

Framework for network-level pavement condition assessment using remote sensing data mining

2020 ◽  
Vol 14 (02) ◽  
pp. 1
Author(s):  
Stefanos S. Politis ◽  
Zhanmin Zhang ◽  
Sareh Kouchaki ◽  
Carlos H. Caldas
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Condition Assessment ◽  
Pavement Condition ◽  
Sensing Data ◽  
Pavement Condition Assessment
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