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    Deep learning–based data anomaly detection for highway slope structural health monitoring: A comparative study

    New search for: Dong, Shi
    New search for: Long, Zhiyou
    New search for: Zhang, Shiyuan
    New search for: Wang, Jianwei
    New search for: Zuo, Chen
    New search for: Yang, Chao
    New search for: Jiang, Jinyi
    New search for: Cui, Zhiwei
    New search for: Wan, Zhaolong

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    Deep learning–based data anomaly detection for highway slope structural health monitoring: A comparative study

    New search for: Dong, Shi
    New search for: Long, Zhiyou
    New search for: Zhang, Shiyuan
    New search for: Wang, Jianwei
    New search for: Zuo, Chen
    New search for: Yang, Chao
    New search for: Jiang, Jinyi
    New search for: Cui, Zhiwei
    New search for: Wan, Zhaolong

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Deep learning–based data anomaly detection for highway slope structural health monitoring: A comparative study

    New search for: Dong, Shi
    New search for: Long, Zhiyou
    New search for: Zhang, Shiyuan
    New search for: Wang, Jianwei
    New search for: Zuo, Chen
    New search for: Yang, Chao
    New search for: Jiang, Jinyi
    New search for: Cui, Zhiwei
    New search for: Wan, Zhaolong

    Access

    Check access
    Check availability in my library
    Order at Subito €

    Access

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    Document information
    Title:

    Deep learning–based data anomaly detection for highway slope structural health monitoring: A comparative study

    Additional title:

    Transportation Geotechnics

    Contributors:

    Dong, Shi (author) / Long, Zhiyou (author) / Zhang, Shiyuan (author) / Wang, Jianwei (author) / Zuo, Chen (author) / Yang, Chao (author) / Jiang, Jinyi (author) / Cui, Zhiwei (author) / Wan, Zhaolong (author)

    Published in:

    Transportation Geotechnics ; 51 ; 101490

    Publication date:

    2025-03-01

    ISSN:

    2214-3912

    DOI:

    https://doi.org/10.1016/j.trgeo.2025.101490

    Type of media:

    Article (Journal)

    Type of material:

    Electronic Resource

    Language:

    English

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