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    Multiway Analytics Applied to Railway Track Geometry and Ballast Conditions

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 001::page 04024079-1
    Author:
    Petros Woldemariam
    ,
    Nii Attoh-Okine
    DOI: 10.1061/AJRUA6.RUENG-1367
    Publisher: American Society of Civil Engineers
    Abstract: Railroad systems generate large amounts of data, which, when effectively analyzed, can significantly enhance maintenance decisions to improve safety and system performance. Tensor decomposition, as an advanced multidimensional data analysis tool, offers unique advantages over traditional two-way matrix factorizations, such as the uniqueness of the optimal solution and component identification, even with substantial data missing. This paper introduces the basic concepts of tensor decomposition and specifically demonstrates its application in analyzing railway track geometry and subsurface conditions. By applying tensor analysis to multidimensional data sets, the study identifies critical patterns in track geometry and ballast conditions. Key findings indicate that tensor-based models can effectively predict track deformations and align maintenance schedules more accurately, thus optimizing repair operations and extending the lifespan of railway infrastructure.
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      Multiway Analytics Applied to Railway Track Geometry and Ballast Conditions

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    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

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    contributor authorPetros Woldemariam
    contributor authorNii Attoh-Okine
    date accessioned2025-04-20T10:34:49Z
    date available2025-04-20T10:34:49Z
    date copyright10/29/2024 12:00:00 AM
    date issued2025
    identifier otherAJRUA6.RUENG-1367.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304993
    description abstractRailroad systems generate large amounts of data, which, when effectively analyzed, can significantly enhance maintenance decisions to improve safety and system performance. Tensor decomposition, as an advanced multidimensional data analysis tool, offers unique advantages over traditional two-way matrix factorizations, such as the uniqueness of the optimal solution and component identification, even with substantial data missing. This paper introduces the basic concepts of tensor decomposition and specifically demonstrates its application in analyzing railway track geometry and subsurface conditions. By applying tensor analysis to multidimensional data sets, the study identifies critical patterns in track geometry and ballast conditions. Key findings indicate that tensor-based models can effectively predict track deformations and align maintenance schedules more accurately, thus optimizing repair operations and extending the lifespan of railway infrastructure.
    publisherAmerican Society of Civil Engineers
    titleMultiway Analytics Applied to Railway Track Geometry and Ballast Conditions
    typeJournal Article
    journal volume11
    journal issue1
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
    identifier doi10.1061/AJRUA6.RUENG-1367
    journal fristpage04024079-1
    journal lastpage04024079-13
    page13
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 001
    contenttypeFulltext
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