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
Parents
125
1–5 of 125
443
3
h-index
3
h-index
3
h-index
3
h-index
3
h-index
1–5 of 443
182

Huazhong University of Science and Technology
Wuhan, China
5 h-index
12 publications
239 citations

Xi'an Jiaotong University
Xi'an, China
4 h-index
7 publications
2K citations
Tongji University
Shanghai, China
3 h-index
6 publications
297 citations

Southeast University
Nanjing, China
3 h-index
12 publications
347 citations

Stevens Institute of Technology
Hoboken, United States
3 h-index
4 publications
104 citations

Griffith University
Brisbane, Australia
3 h-index
12 publications
470 citations

Northwestern Polytechnical University
Xi'an, China
3 h-index
8 publications
333 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
7 publications
185 citations
1–8 of 182
12 publications · 1.1K citations
12
h-index
6 publications · 332 citations
6
h-index
6 publications · 649 citations
6
h-index
5 publications · 159 citations
5
h-index
MSSP · 3 publications · 117 citations
3
h-index
1–5