Data-driven Deterioration Prediction

Data-Driven Deterioration Prediction is a computational framework that distinguishes itself from conventional deterioration modeling approaches by leveraging empirical data and statistical learning techniques to infer degradation patterns and forecast asset condition states without relying primarily on mechanistic or rule-based assumptions.

125

Publications

5.5K

Citations

443

Authors

182

Institutions

Publications per year

2017–2026

84

Authors

443

  1. KL
    Kaijian Liu

    Stevens Institute of Technology, United States

    3 publications · 79 citations

    3

    h-index

  2. SS
    Sujeeva Setunge

    RMIT University, Australia

    3 publications · 56 citations

    3

    h-index

  3. YL
    Yew‐Chaye Loo

    Griffith University, Australia

    3 publications · 108 citations

    3

    h-index

  4. MB
    Michael Blumenstein

    Griffith University, Australia

    3 publications · 108 citations

    3

    h-index

  5. HG
    Hong Guan

    Griffith University, Australia

    3 publications · 108 citations

    3

    h-index

1–5 of 443

Venues

  1. Reliability Engineering & System Safety

    12 publications · 1.1K citations

    12

    h-index

  2. 6

    h-index

  3. Journal of Infrastructure Systems

    6 publications · 649 citations

    6

    h-index

  4. Structure and Infrastructure Engineering

    5 publications · 159 citations

    5

    h-index

  5. Mechanical Systems and Signal Processing

    MSSP · 3 publications · 117 citations

    3

    h-index

1–5