Detection of Pavement Maintenance Treatments using Deep-Learning Network

Pavement maintenance and rehabilitation (M&R) records are important as they provide documentation that M&R treatment is being performed and completed appropriately. Moreover, the development of pavement performance models relies heavily on the quality of the condition data collected and on the M&R records. However, the history of pavement M&R activities is often missing or unavailable to highway agencies for many reasons. Without accurate M&R records, it is difficult to determine if a condition change between two consecutive inspections is the result of M&R intervention, deterioration, or measurement errors. In this paper, we employed deep-learning networks of a convolutional neural network (CNN) model, a long short-term memory (LSTM) model, and a CNN-LSTM combination model to automatically detect if an M&R treatment was applied to a pavement section during a given time period. Unlike conventional analysis methods so far followed, deep-learning techniques do not require any feature extraction. The maximum accuracy obtained for test data is 87.5% using CNN-LSTM.

• Record URL:
  • https://doi.org/10.1177/03611981211007846
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • All opinions, errors, omissions, and recommendations in this paper are the responsibility of the authors. © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Gao, Lu
  • Yu, Yao
  • Hao Ren, Yi
  • Lu, Pan
• Publication Date: 2021-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1434-1443
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 9
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Condition surveys; Detection and identification; History; Machine learning; Neural networks; Pavement maintenance
• Subject Areas: Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01763739
• Record Type: Publication
• Report/Paper Numbers: TRBAM-21-02107
• Files: TRIS, TRB, ATRI
• Created Date: Feb 4 2021 10:57AM