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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:

Deep learning algorithm for real-time automatic crack detection, segmentation, qualification
Gang Xu, Qingrui Yue, Xiaogang Liu
Engineering Applications of Artificial Intelligence (2023) Vol. 126, pp. 107085-107085
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Network for robust and high-accuracy pavement crack segmentation
Yingchao Zhang, Cheng Liu
Automation in Construction (2024) Vol. 162, pp. 105375-105375
Closed Access | Times Cited: 12
Deep learning in fringe projection: A review
Haoyue Liu, Ning Yan, Bofan Shao, et al.
Neurocomputing (2024) Vol. 581, pp. 127493-127493
Closed Access | Times Cited: 9
Real-time multi-object detection model for cracks and deformations based on deep learning
Gang Xu, Qingrui Yue, Xiaogang Liu
Advanced Engineering Informatics (2024) Vol. 61, pp. 102578-102578
Closed Access | Times Cited: 8
Segment-to-track for pavement crack with light-weight neural network on unmanned wheeled robot
Jianqi Zhang, Xu Yang, Wei Wang, et al.
Automation in Construction (2024) Vol. 161, pp. 105346-105346
Closed Access | Times Cited: 7
Deep learning-assisted automatic quality assessment of concrete surfaces with cracks and bugholes
Jiepeng Liu, Zhengtao Yang, Hongtuo Qi, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102577-102577
Closed Access | Times Cited: 7
A comparison study of semantic segmentation networks for crack detection in construction materials
Zhongqi Shi, Nan Jin, Dongbo Chen, et al.
Construction and Building Materials (2024) Vol. 414, pp. 134950-134950
Closed Access | Times Cited: 6
Identification of historical maximum load on concrete beams based on crack and deformation response using knowledge-embedded machine learning algorithms
Gang Xu, Qingrui Yue, Xiaogang Liu
Engineering Structures (2024) Vol. 314, pp. 118376-118376
Closed Access | Times Cited: 4
Automatic detection of subsurface defects in concrete structures using state-of-the-art deep learning-based object detectors on the infrared dataset
Quang Tai Ta, Van Ha Mac, Jungwon Huh, et al.
Engineering Structures (2025) Vol. 329, pp. 119829-119829
Closed Access
SBDNet: A deep learning-based method for the segmentation and quantification of fatigue cracks in steel bridges
Xiaodong Wang, Qingrui Yue, Xiaogang Liu
Advanced Engineering Informatics (2025) Vol. 65, pp. 103186-103186
Closed Access
DCMA-Net: A dual channel multi-scale feature attention network for crack image segmentation
Yidan Yan, Junding Sun, Hongyuan Zhang, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110411-110411
Closed Access
Crack segmentation‐guided measurement with lightweight distillation network on edge device
Jianqi Zhang, Ling Ding, Wei Wang, et al.
Computer-Aided Civil and Infrastructure Engineering (2025)
Open Access
Imaging‐Based Instance Segmentation of Pavement Cracks Using an Improved YOLOv8 Network
Fei Yu, Guanting Ye, Qing Jiang, et al.
Structural Control and Health Monitoring (2025) Vol. 2025, Iss. 1
Open Access
A deep learning-based adaptive denoising approach for fine identification of rock microcracks from noisy strain data
Shuai Zhao, Dian-Rui Mu, Dao-Yuan Tan
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110471-110471
Closed Access
BBCNet: Boundary-Body Coherence Network with Adaptive Self-Attention Distillation for Enhanced Crack Segmentation
Xiao Hu, Kang Liu, Zhihao Xv
Digital Signal Processing (2025), pp. 105148-105148
Closed Access
A review of crack research in concrete structures based on data-driven and intelligent algorithms
Congcong Fan, Youliang Ding, Xujia Liu, et al.
Structures (2025) Vol. 75, pp. 108800-108800
Closed Access
Enhancing pixel-level crack segmentation with visual mamba and convolutional networks
Chengjia Han, Handuo Yang, Yaowen Yang
Automation in Construction (2024) Vol. 168, pp. 105770-105770
Closed Access | Times Cited: 3
Crack detection based on attention mechanism with YOLOv5
Min‐Li Lan, Dan Yang, Shuangxi Zhou, et al.
Engineering Reports (2024)
Open Access | Times Cited: 2
Deep learning approaches for autonomous crack detection in concrete wall, brick deck and pavement
Fethi Şermet, İshak Paçal
DÜMF Mühendislik Dergisi (2024)
Open Access | Times Cited: 2
AI-Driven Non-Destructive Testing Insights
Amine el Mahdi Safhi, Gilberto Cidreira Keserle, Stéphanie C. Blanchard
Encyclopedia (2024) Vol. 4, Iss. 4, pp. 1760-1769
Open Access | Times Cited: 2
GAF-Net: A New Automated Segmentation Method Based on Multiscale Feature Fusion and Feedback module
Long Wen, Yuxing Ye, Lei Zuo
Pattern Recognition Letters (2024)
Closed Access | Times Cited: 2
Semi-supervised crack detection using segment anything model and deep transfer learning
Jiale Li, Chenglong Yuan, Xuefei Wang, et al.
Automation in Construction (2024) Vol. 170, pp. 105899-105899
Closed Access | Times Cited: 2
Use of Cohesive Approaches for Modelling Critical States in Fibre Reinforced Structural Materials
Vladislav Kozák, Jiřı́ Vala
(2024)
Open Access | Times Cited: 1
Accelerating Image Analysis in Immersive Devices Using Automatic Region Selection
Kaveh Malek, Mahsa Sanei, Ali Mohammadkhorasani, et al.
(2024)
Closed Access
Use of Cohesive Approaches for Modelling Critical States in Fibre-Reinforced Structural Materials
Vladislav Kozák, Jiřı́ Vala
Materials (2024) Vol. 17, Iss. 13, pp. 3177-3177
Open Access
Advanced Crack Detection in Bidirectional Gradient Material FGM Beams: A Neural Network Approach with Adam Optimization
Anass Moukhliss, Nassima Ayoub, Abdellatif Rahmouni, et al.
Iranian Journal of Science and Technology Transactions of Mechanical Engineering (2024)
Closed Access
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