Bi-Directional Image-to-Text Mapping for NLP-Based Schedule Generation and Computer Vision Progress Monitoring-Reference-Cited by-同舟云学术

Bi-Directional Image-to-Text Mapping for NLP-Based Schedule Generation and Computer Vision Progress Monitoring

Published:2024-03-18 Issue: Volume:1 Page:826-835
ISSN:
Container-title:Construction Research Congress 2024
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Author:

Núñez-Morales Juan D.¹²³,Jung Yoonhwa¹²³,Golparvar-Fard Mani¹²³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, and Computer Science, Univ. of Illinois at Urbana-Champaign, Urbana, IL.

2. Ph.D. Student, Dept. of Civil and Environmental Engineering, and Computer Science, Univ. of Illinois at Urbana-Champaign, Urbana, IL.

3. Professor, Dept. of Civil and Environmental Engineering, and Computer Science, Univ. of Illinois at Urbana-Champaign, Urbana, IL.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485262.084

Reference51 articles.

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2. Alikhani H. Le C. and Jeong H. D. (2020). “A deep learning algorithms to generate activity sequences using historical as-built schedule data.” Creative Construction e-Conference 2020 Budapest University of Technology and Economics 2–6 (July).

3. Automatic understanding of construction schedules: Part-of-activity tagging;Amer F.;EC3 Conference 2019,2019

4. Construction schedule augmentation with implicit dependency constraints and automated generation of lookahead plan revisions

5. Automated Methods and Systems for Construction Planning and Scheduling: Critical Review of Three Decades of Research

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