Computer Vision–Based Model for Moisture Marks Detection and Recognition in Subway Networks-Reference-Cited by-同舟云学术

Computer Vision–Based Model for Moisture Marks Detection and Recognition in Subway Networks

Published:2018-03 Issue:2 Volume:32 Page:04017079
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Dawood Thikra¹^ORCID,Zhu Zhenhua²,Zayed Tarek³

Affiliation:

1. Ph.D. Candidate, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1515 Sainte-Catherine St. W., Montréal, Canada H3G 2W1 (corresponding author). ORCID: .

2. Associate Professor, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1515 Sainte-Catherine St. W., Montréal, Canada H3G 2W1.

3. Professor, Dept. of Building, Civil and Environmental Engineering, Concordia Univ., 1515 Sainte-Catherine St. W., Montréal, Canada H3G 2W1.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000728

Reference48 articles.

1. Abouhamad M. (2014). “An integrated risk-based asset management framework for subway systems.” Ph.D. thesis Concordia Univ. Montréal.

2. An imaging data model for concrete bridge inspection

3. Abu-Mallouh M. (1999). “Model for station rehabilitation and planning (MSRP).” Ph.D. thesis Polytechnic Univ. New York.

4. Adhikari R. S. Moselhi O. and Bagchi A. (2013a). “A study of image-based element condition index for bridge inspection.” Proc. 30th Int. Symp. on Automation and Robotics in Construction (ISARC 2013) International Association for Automation and Robotics in Construction Waterloo Canada 345–356.

5. Image-based retrieval of concrete crack properties for bridge inspection

Cited by 29 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. From classification to segmentation with explainable AI: A study on crack detection and growth monitoring;Automation in Construction;2024-09

2. Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification;Buildings;2024-06-03

3. A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors;IEEE Access;2024

4. A hybrid attention deep learning network for refined segmentation of cracks from shield tunnel lining images;Journal of Rock Mechanics and Geotechnical Engineering;2023-12

5. Utilizing different artificial intelligence techniques for efficient condition assessment of building components;Canadian Journal of Civil Engineering;2023-09-15