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
Parents
96
Observational Procedure of Settlement Prediction
Akira Asaoka · SOILS AND FOUNDATIONS · 1978 · 586 citations
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170
Tongji University
Shanghai, China
8 h-index
23 publications
812 citations

Zhejiang University
Hangzhou, China
6 h-index
14 publications
851 citations

Central South University
Changsha, China
5 h-index
16 publications
483 citations
Ruhr University Bochum
Bochum, Germany
4 h-index
11 publications
356 citations

Hunan University
Changsha, China
4 h-index
17 publications
3K citations

Université Grenoble Alpes
Saint-Martin-d'Hères, France
3 h-index
5 publications
223 citations
New York University
New York, United States
3 h-index
10 publications
458 citations

Sichuan University
Chengdu, China
3 h-index
7 publications
122 citations
1–8 of 170
9 publications · 723 citations
9
h-index
9 publications · 716 citations
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8 publications · 996 citations
8
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JRMGE · 4 publications · 442 citations
4
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4 publications · 106 citations
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1–5