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    A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction

    Source: Journal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003::page 04024029-1
    Author:
    Mostafa A. Elseifi
    ,
    Md. Tanvir Ahmed Sarkar
    ,
    Ramchandra Paudel
    ,
    Hossam Abohamer
    ,
    Momen R. Mousa
    DOI: 10.1061/JPEODX.PVENG-1411
    Publisher: American Society of Civil Engineers
    Abstract: Pavement construction monitoring and quality assurance (QA) practices are based mostly on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in the case of the availability of an inertial profiler. However, most of these practices are costly and/or destructive. Therefore, the key objective of this study was to develop a quality assurance decision-making tool that can predict pavement roughness, in terms of the International Roughness Index (IRI), and aggregate segregation based on digital image analysis, image recognition, and deep machine learning models. The developed models were trained, tested, and validated using 600 pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS). Furthermore, the effectiveness of the convolution neural network (CNN) model was validated using pavement surface images collected at construction sites in Louisiana a few days after paving. The roughness model predicted the International Roughness Index values with a coefficient of determination R2 of 0.98 and a RMS error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved acceptable accuracy. To support the implementation of these results, the models were incorporated into a computer application that can be used by site engineers for quality assurance without the need for coding software on their device.
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      A Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4298089
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    • Journal of Transportation Engineering, Part B: Pavements

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    contributor authorMostafa A. Elseifi
    contributor authorMd. Tanvir Ahmed Sarkar
    contributor authorRamchandra Paudel
    contributor authorHossam Abohamer
    contributor authorMomen R. Mousa
    date accessioned2024-12-24T09:59:30Z
    date available2024-12-24T09:59:30Z
    date copyright9/1/2024 12:00:00 AM
    date issued2024
    identifier otherJPEODX.PVENG-1411.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4298089
    description abstractPavement construction monitoring and quality assurance (QA) practices are based mostly on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in the case of the availability of an inertial profiler. However, most of these practices are costly and/or destructive. Therefore, the key objective of this study was to develop a quality assurance decision-making tool that can predict pavement roughness, in terms of the International Roughness Index (IRI), and aggregate segregation based on digital image analysis, image recognition, and deep machine learning models. The developed models were trained, tested, and validated using 600 pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS). Furthermore, the effectiveness of the convolution neural network (CNN) model was validated using pavement surface images collected at construction sites in Louisiana a few days after paving. The roughness model predicted the International Roughness Index values with a coefficient of determination R2 of 0.98 and a RMS error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved acceptable accuracy. To support the implementation of these results, the models were incorporated into a computer application that can be used by site engineers for quality assurance without the need for coding software on their device.
    publisherAmerican Society of Civil Engineers
    titleA Machine Learning–Based Framework for Predicting Pavement Roughness and Aggregate Segregation during Construction
    typeJournal Article
    journal volume150
    journal issue3
    journal titleJournal of Transportation Engineering, Part B: Pavements
    identifier doi10.1061/JPEODX.PVENG-1411
    journal fristpage04024029-1
    journal lastpage04024029-9
    page9
    treeJournal of Transportation Engineering, Part B: Pavements:;2024:;Volume ( 150 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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