YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASCE
    • Journal of Materials in Civil Engineering
    • View Item
    •   YE&T Library
    • ASCE
    • Journal of Materials in Civil Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Automated Mineral Identification of Pozzolanic Materials Using XRD Patterns

    Source: Journal of Materials in Civil Engineering:;2024:;Volume ( 036 ):;issue: 012::page 04024415-1
    Author:
    Hyunjun Kim
    ,
    Jinyoung Yoon
    DOI: 10.1061/JMCEE7.MTENG-17973
    Publisher: American Society of Civil Engineers
    Abstract: Use of pozzolanic materials in concrete has become increasingly popular due to their ability to improve long-term strength and durability. To better understand their reactivity and reaction mechanism, proper characterization of their chemical properties is required. X-ray diffraction (XRD) is a widely used nondestructive testing method that can identify the different constituent phases of pozzolanic materials. However, the conventional qualitative approach for XRD analysis can be challenging due to the significant peak overlap and an amorphous hump in the baseline of XRD patterns. To address this issue, this study proposes a deep learning–based automated method for accurately identifying the minerals in pozzolanic materials using XRD patterns. The proposed method involves the deep learning–based peak picking algorithm, establishment of mineral candidates, and automated mineral identification using an optimization process. The performance of the model was validated using eight pozzolanic materials belonging to four classes (slag, fly ash, ground bottom ash, and silica fume).
    • Download: (4.168Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Automated Mineral Identification of Pozzolanic Materials Using XRD Patterns

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4304144
    Collections
    • Journal of Materials in Civil Engineering

    Show full item record

    contributor authorHyunjun Kim
    contributor authorJinyoung Yoon
    date accessioned2025-04-20T10:10:36Z
    date available2025-04-20T10:10:36Z
    date copyright9/27/2024 12:00:00 AM
    date issued2024
    identifier otherJMCEE7.MTENG-17973.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304144
    description abstractUse of pozzolanic materials in concrete has become increasingly popular due to their ability to improve long-term strength and durability. To better understand their reactivity and reaction mechanism, proper characterization of their chemical properties is required. X-ray diffraction (XRD) is a widely used nondestructive testing method that can identify the different constituent phases of pozzolanic materials. However, the conventional qualitative approach for XRD analysis can be challenging due to the significant peak overlap and an amorphous hump in the baseline of XRD patterns. To address this issue, this study proposes a deep learning–based automated method for accurately identifying the minerals in pozzolanic materials using XRD patterns. The proposed method involves the deep learning–based peak picking algorithm, establishment of mineral candidates, and automated mineral identification using an optimization process. The performance of the model was validated using eight pozzolanic materials belonging to four classes (slag, fly ash, ground bottom ash, and silica fume).
    publisherAmerican Society of Civil Engineers
    titleAutomated Mineral Identification of Pozzolanic Materials Using XRD Patterns
    typeJournal Article
    journal volume36
    journal issue12
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/JMCEE7.MTENG-17973
    journal fristpage04024415-1
    journal lastpage04024415-14
    page14
    treeJournal of Materials in Civil Engineering:;2024:;Volume ( 036 ):;issue: 012
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian