Machine Learning-based Assessment

Machine Learning-Based Assessment is a structural condition evaluation methodology that employs machine learning algorithms to analyze sensor data, identify patterns, and predict structural health states, distinguishing it from traditional assessment approaches by its reliance on data-driven modeling and adaptive learning capabilities.

236

Publications

11.9K

Citations

1.1K

Authors

524

Institutions

Publications per year

2017–2026

206

Authors

1.1K

  1. DJ
    Diane J. Cook

    Washington State University, United States

    3 publications · 221 citations

    3

    h-index

  2. JK
    Joseph Krajcik

    University of Michigan, United States

    3 publications · 144 citations

    3

    h-index

  3. SM
    Sujith Mangalathu

    Hanyang University, South Korea

    3 publications · 227 citations

    3

    h-index

  4. KY
    Ka‐Veng Yuen

    University of Macau, Macao SAR China

    3 publications · 120 citations

    3

    h-index

  5. HS
    Hadi Salehi

    Michigan State University, United States

    3 publications · 93 citations

    3

    h-index

1–5 of 1.1K

Venues

  1. Sensors

    11 publications · 301 citations

    10

    h-index

  2. IEEE Access

    9 publications · 380 citations

    9

    h-index

  3. Structural Health Monitoring

    SHM · 7 publications · 165 citations

    7

    h-index

  4. Scientific Reports

    6 publications · 314 citations

    6

    h-index

  5. Applied Sciences

    5 publications · 147 citations

    5

    h-index

1–5