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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Automatic Crack Detection and Measurement of Concrete Structure Using Convolutional Encoder-Decoder Network
Shengyuan Li, Xuefeng Zhao
IEEE Access (2020) Vol. 8, pp. 134602-134618
Open Access | Times Cited: 57

Showing 1-25 of 57 citing articles:

Structural crack detection using deep convolutional neural networks
Ali Raza, Joon Huang Chuah, Mohamad Sofian Abu Talip, et al.
Automation in Construction (2021) Vol. 133, pp. 103989-103989
Closed Access | Times Cited: 233
Computer Vision Applications in Intelligent Transportation Systems: A Survey
Esma Dilek, Murat Dener
Sensors (2023) Vol. 23, Iss. 6, pp. 2938-2938
Open Access | Times Cited: 50
Automated defect inspection of concrete structures
Jun Kang Chow, Kuan-Fu Liu, Pin Siang Tan, et al.
Automation in Construction (2021) Vol. 132, pp. 103959-103959
Closed Access | Times Cited: 65
A review of the research and application of deep learning-based computer vision in structural damage detection
Lingxin Zhang, Junkai Shen, Zhu Baijie
Earthquake Engineering and Engineering Vibration (2022) Vol. 21, Iss. 1, pp. 1-21
Closed Access | Times Cited: 58
Deep learning-based concrete defects classification and detection using semantic segmentation
Palisa Arafin, A. H. M. Muntasir Billah, Anas Issa
Structural Health Monitoring (2023) Vol. 23, Iss. 1, pp. 383-409
Open Access | Times Cited: 35
Image Processing Techniques for Concrete Crack Detection: A Scientometrics Literature Review
Md. Al-Masrur Khan, Seong‐Hoon Kee, Al‐Sakib Khan Pathan, et al.
Remote Sensing (2023) Vol. 15, Iss. 9, pp. 2400-2400
Open Access | Times Cited: 28
Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022
Luqman Ali, Fady Alnajjar, Wasif Khan, et al.
Buildings (2022) Vol. 12, Iss. 4, pp. 432-432
Open Access | Times Cited: 34
Smart Crack Detection System Using Nanostructured Materials with Integrated Optimization Technology
Abhijeet Rajendra Pabale, Rushikesh Vilas Kolhe, P. William, et al.
Journal of Nano- and Electronic Physics (2023) Vol. 15, Iss. 4, pp. 04019-5
Open Access | Times Cited: 20
Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment
Jingyue Yuan, Qiubing Ren, Chao Jia, et al.
Structures (2023) Vol. 59, pp. 105780-105780
Closed Access | Times Cited: 20
CNN-Based Image Quality Classification Considering Quality Degradation in Bridge Inspection Using an Unmanned Aerial Vehicle
Gi-Hun Gwon, Jin-Hwan Lee, In‐Ho Kim, et al.
IEEE Access (2023) Vol. 11, pp. 22096-22113
Open Access | Times Cited: 16
Deep Learning for Concrete Crack Detection and Measurement
Mthabisi Adriano Nyathi, J. Bai, Ian Wilson
Metrology (2024) Vol. 4, Iss. 1, pp. 66-81
Open Access | Times Cited: 6
Deep neural networks for crack detection inside structures
Fatahlla Moreh, Hao Lyu, Zarghaam Haider Rizvi, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Crack Segmentation Network using Additive Attention Gate—CSN-II
Ali Raza, Joon Huang Chuah, Mohamad Sofian Abu Talip, et al.
Engineering Applications of Artificial Intelligence (2022) Vol. 114, pp. 105130-105130
Closed Access | Times Cited: 25
A Novel Approach for UAV Image Crack Detection
Yanxiang Li, Jinming Ma, Ziyu Zhao, et al.
Sensors (2022) Vol. 22, Iss. 9, pp. 3305-3305
Open Access | Times Cited: 24
Sam-based instance segmentation models for the automation of structural damage detection
Zehao Ye, Lucy Lovell, Asaad Faramarzi, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102826-102826
Closed Access | Times Cited: 5
Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics
Tan Qin, Gongxing Yan, H. J. Jiang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Deep Learning-Enabled Health Assessment for Sustainable Maintenance of Existing Concrete Structures: A Review
Pankaj Panwar, K. L. Goyal, Jatin Kumar Shandilya
Springer tracts in civil engineering (2025), pp. 93-121
Closed Access
Application of deep learning in structural health management of concrete structures
Ikenna D. Uwanuakwa, John Bush Idoko, Elvis Michael Mbadike, et al.
Proceedings of the Institution of Civil Engineers - Bridge Engineering (2022) Vol. 177, Iss. 2, pp. 99-106
Closed Access | Times Cited: 19
An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images
Chow Jun Kang, Wong Cho Hin Peter, Tan Pin Siang, et al.
Structural Health Monitoring (2023) Vol. 22, Iss. 5, pp. 3320-3337
Closed Access | Times Cited: 11
Cracklab: A high-precision and efficient concrete crack segmentation and quantification network
Zhenwei Yu, Yonggang Shen, Zhilin Sun, et al.
Developments in the Built Environment (2022) Vol. 12, pp. 100088-100088
Closed Access | Times Cited: 17
Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
Mthabisi Adriano Nyathi, J. Bai, Ian Wilson
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 4981-4981
Open Access | Times Cited: 9
Machine-Aided Bridge Deck Crack Condition State Assessment Using Artificial Intelligence
Xin Zhang, Benjamin E. Wogen, Xiaoyu Liu, et al.
Sensors (2023) Vol. 23, Iss. 9, pp. 4192-4192
Open Access | Times Cited: 9
HBIM for Conservation of Built Heritage
Yahya Alshawabkeh, Ahmad Baik, Yehia Miky
ISPRS International Journal of Geo-Information (2024) Vol. 13, Iss. 7, pp. 231-231
Open Access | Times Cited: 3
Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification
Palisa Arafin, Anas Issa, A. H. M. Muntasir Billah
Sensors (2022) Vol. 22, Iss. 22, pp. 8714-8714
Open Access | Times Cited: 15
Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models
Mohammed Ameen Mohammed, Zheng Han, Yange Li
Advances in Materials Science and Engineering (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 18
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