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Detection of Pavement Maintenance Treatments using Deep-Learning Network
- Source :
- Transportation Research Record: Journal of the Transportation Research Board. 2675:1434-1443
- Publication Year :
- 2021
- Publisher :
- SAGE Publications, 2021.
-
Abstract
- 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.
- Subjects :
- 050210 logistics & transportation
Rehabilitation
business.industry
Computer science
Mechanical Engineering
Deep learning
medicine.medical_treatment
05 social sciences
0211 other engineering and technologies
Pavement maintenance
02 engineering and technology
Construction engineering
Documentation
021105 building & construction
0502 economics and business
medicine
Artificial intelligence
business
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 21694052 and 03611981
- Volume :
- 2675
- Database :
- OpenAIRE
- Journal :
- Transportation Research Record: Journal of the Transportation Research Board
- Accession number :
- edsair.doi...........f40b181994e20cc5491ba834cddb1b97