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

Authors

1.7K

  1. HL
    Heng Li

    Hong Kong Polytechnic University, Hong Kong SAR China

    10 publications · 818 citations

    10

    h-index

  2. BX
    Bo Xiao

    University of Alberta, Canada

    9 publications · 825 citations

    9

    h-index

  3. PE
    Peter E.D. Love

    Curtin University, Australia

    7 publications · 883 citations

    7

    h-index

  4. HL
    Hanbin Luo

    Huazhong University of Science and Technology, China

    7 publications · 925 citations

    7

    h-index

  5. LD
    Lieyun Ding

    Huazhong University of Science and Technology, China

    7 publications · 1.2K citations

    7

    h-index

1–5 of 1.7K

Venues

  1. Automation in Construction

    91 publications · 8.5K citations

    54

    h-index

  2. Advanced Engineering Informatics

    23 publications · 2.2K citations

    22

    h-index

  3. Construction and Building Materials

    19 publications · 1.3K citations

    18

    h-index

  4. IEEE Access

    16 publications · 689 citations

    16

    h-index

  5. Journal of Computing in Civil Engineering

    16 publications · 1.5K citations

    16

    h-index

1–5