Data-driven Settlement Prediction

Data-Driven Settlement Prediction is a computational modeling approach that distinguishes itself by utilizing empirical monitoring data and statistical or machine learning techniques to forecast tunnel-induced ground settlements, as opposed to relying primarily on theoretical or analytical methods.

96

Publications

6.5K

Citations

365

Authors

170

Institutions

Publications per year

2017–2026

70

Authors

365

  1. RC
    Renpeng Chen

    Hunan University, China

    4 publications · 721 citations

    4

    h-index

  2. HH
    Hongwei Huang

    Tongji University, China

    3 publications · 125 citations

    3

    h-index

  3. DZ
    Dongming Zhang

    Tongji University, China

    3 publications · 125 citations

    3

    h-index

  4. PZ
    Pin Zhang

    Hong Kong Polytechnic University, Hong Kong SAR China

    3 publications · 696 citations

    3

    h-index

  5. HW
    Huai-Na Wu

    Hunan University, China

    3 publications · 696 citations

    3

    h-index

1–5 of 365

Venues

  1. Automation in Construction

    9 publications · 723 citations

    9

    h-index

  2. Computers and Geotechnics

    9 publications · 716 citations

    9

    h-index

  3. Tunnelling and Underground Space Technology

    8 publications · 996 citations

    8

    h-index

  4. Journal of Rock Mechanics and Geotechnical Engineering

    JRMGE · 4 publications · 442 citations

    4

    h-index

  5. Sustainability

    4 publications · 106 citations

    4

    h-index

1–5