scholarly journals Do vehicles sense pavement surface anomalies?

Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou
Keyword(s):  

2021 ◽  
Vol 148 ◽  
pp. 79-99
Author(s):  
Julie Yu Qiao ◽  
Runjia Du ◽  
Samuel Labi ◽  
Jon D. Fricker ◽  
Kumares C. Sinha


2021 ◽  
Vol 128 ◽  
pp. 103221
Author(s):  
Allen A. Zhang ◽  
Guangwei Yang ◽  
Kelvin C.P. Wang ◽  
Baoxian Li ◽  
Haiwang Kong ◽  
...  


Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou

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.



Author(s):  
Faranak Hosseini ◽  
S. M. Kamal Hossain ◽  
Liping Fu ◽  
Marc Johnson ◽  
Yuheng Fei




Transport ◽  
2016 ◽  
Vol 31 (2) ◽  
pp. 177-182 ◽  
Author(s):  
Mario De Luca ◽  
Francesco Abbondati ◽  
Thomas J. Yager ◽  
Gianluca Dell’Acqua

Surfaces of airport pavements are subject to contamination that can be very dangerous for the movement of aircraft particularly on the runway. A recurrent problem is represented by the deposits of vulcanized rubber of aircraft tires in the touchdown area during landings and lesser during take-offs. This causes a loss of grip that compromises the safety of aircraft movements in take-off and landing operations. This study deals with the surface characteristics decay phenomenon related to contamination from rubber deposits. The experiment was conducted by correlating the pavement surface characteristics, as detected by Grip Tester, to air traffic before and after de-rubberizing operation and two models were constructed for the assessment of functional capacity of the runway before and after the operations de-rubberizing.







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