Deep Learning In Construction
Deep Learning in Construction is a scholarly contribution in the field of construction automation that employs deep neural networks to model, analyze, or optimize construction processes, distinguishing itself by leveraging hierarchical feature learning from large-scale construction-related data.
478
Publications
27.3K
Citations
1.7K
Authors
555
Institutions
Publications per year
2017–2026
464
Parents
478
Shanaka Kristombu Baduge, Sadeep Thilakarathna, Jude Shalitha Perera et al. · Automation in Construction · 2022 · 916 citations · Full text
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Hong Kong Polytechnic University
Hong Kong, Hong Kong
29 h-index
68 publications
4.6K citations

Huazhong University of Science and Technology
Wuhan, China
22 h-index
69 publications
7.5K citations
Tongji University
Shanghai, China
19 h-index
59 publications
3.6K citations
Yonsei University
Seoul, South Korea
12 h-index
31 publications
2.7K citations

University of Alberta
Edmonton, Canada
11 h-index
27 publications
1.2K citations

Chongqing University
Chongqing, China
10 h-index
34 publications
1.3K citations
Tianjin University
Tianjin, China
10 h-index
30 publications
1.4K citations
ORCID
Bethesda, United States
9 h-index
17 publications
835 citations
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91 publications · 8.5K citations
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23 publications · 2.2K citations
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19 publications · 1.3K citations
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16 publications · 689 citations
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16 publications · 1.5K citations
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