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.


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    Title :

    Detection of Pavement Maintenance Treatments using Deep-Learning Network


    Additional title:

    Transportation Research Record


    Contributors:
    Gao, Lu (author) / Yu, Yao (author) / Hao Ren, Yi (author) / Lu, Pan (author)


    Publication date :

    2021-04-04




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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