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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:

Structural damage-causing concrete cracking detection based on a deep-learning method
Xiaojian Han, Zhicheng Zhao, Lingkun Chen, et al.
Construction and Building Materials (2022) Vol. 337, pp. 127562-127562
Closed Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN
Ruoxian Li, Jia‐Yong Yu, Feng Li, et al.
Construction and Building Materials (2022) Vol. 362, pp. 129659-129659
Closed Access | Times Cited: 111
Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning
K C Laxman, Nishat Tabassum, Li Ai, et al.
Construction and Building Materials (2023) Vol. 370, pp. 130709-130709
Closed Access | Times Cited: 82
Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification
Sandra Matarneh, Faris Elghaish, Farzad Pour Rahimian, et al.
Automation in Construction (2024) Vol. 160, pp. 105297-105297
Open Access | Times Cited: 26
Binocular Video-Based Automatic Pixel-Level Crack Detection and Quantification Using Deep Convolutional Neural Networks for Concrete Structures
Li Liu, Bo Shen, Shuchen Huang, et al.
Buildings (2025) Vol. 15, Iss. 2, pp. 258-258
Open Access | Times Cited: 3
Convolutional neural networks (CNNs)-based multi-category damage detection and recognition of high-speed rail (HSR) reinforced concrete (RC) bridges using test images
Lingkun Chen, Wenxin Chen, Lu Wang, et al.
Engineering Structures (2022) Vol. 276, pp. 115306-115306
Closed Access | Times Cited: 61
Multiclass seismic damage detection of buildings using quantum convolutional neural network
Sanjeev Bhatta, Ji Dang
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 3, pp. 406-423
Open Access | Times Cited: 26
Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review
Dariush Amirkhani, Mohand Saïd Allili, Loucif Hebbache, et al.
IEEE Transactions on Intelligent Transportation Systems (2024) Vol. 25, Iss. 9, pp. 10483-10505
Closed Access | Times Cited: 12
Seismic Damage Quantification of RC Short Columns from Crack Images Using the Enhanced U-Net
Zefan Chen, Qian Chen, Zhangjun Dai, et al.
Buildings (2025) Vol. 15, Iss. 3, pp. 322-322
Open Access | Times Cited: 1
Seismic damage recognition of structural and non-structural components based on convolutional neural networks
Weiping Wen, Tao Xu, Jie Hu, et al.
Journal of Building Engineering (2025) Vol. 102, pp. 112012-112012
Closed Access
Machine learning-based techniques for marine structures: A state-of-the-art review
Xiaoguang Zhou, Chao Hou, Yantao Yu, et al.
(2025)
Closed Access
MDCCM: a lightweight multi-scale model for high-accuracy pavement crack detection
Zhuo Gu, Tao Li, Qiang Xiao, et al.
Signal Image and Video Processing (2025) Vol. 19, Iss. 6
Closed Access
Performance of Fine-Tuning Techniques for Multilabel Classification of Surface Defects in Reinforced Concrete Bridges
Benyamin Pooraskarparast, Ngoc-Son Dang, Vikram Pakrashi, et al.
Applied Sciences (2025) Vol. 15, Iss. 9, pp. 4725-4725
Open Access
Condition assessment of prestressed hollow-core slab bridges based on 1D CNN using acoustic emission technique
Shengli Li, Ren He, Yifei Zhang, et al.
Nondestructive Testing And Evaluation (2025), pp. 1-20
Closed Access
Transfer learning for smart construction: Advances and future directions
Gao Yu, Xiaoxiao Xu, Tak Wing Yiu, et al.
Automation in Construction (2025) Vol. 175, pp. 106238-106238
Closed Access
Impact and Failure Analysis of U-Shaped Concrete Containing Polyurethane Materials: Deep Learning and Digital Imaging Correlation-Based Approach
Saleh Ahmad Laqsum, Han Zhu, Sadi Ibrahim Haruna, et al.
Polymers (2025) Vol. 17, Iss. 9, pp. 1245-1245
Open Access
Ultrasound classification of interacting flaws using finite element simulations and convolutional neural network
Sijun Niu, Vikas Srivastava
Engineering With Computers (2022) Vol. 38, Iss. 5, pp. 4653-4662
Closed Access | Times Cited: 17
Crack detection for concrete bridges with imaged based deep learning
Chunfeng Wan, Xiaobing Xiong, Bo Wen, et al.
Science Progress (2022) Vol. 105, Iss. 4
Open Access | Times Cited: 17
Visualization analysis of concrete crack detection in civil engineering infrastructure based on knowledge graph
Wei Chen, Jia Hou, Yanhua Wang, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03711-e03711
Open Access | Times Cited: 3
Method for Concrete Structure Analysis by Microscopy of Hardened Cement Paste and Crack Segmentation Using a Convolutional Neural Network
Alexey N. Beskopylny, Evgenii M. Shcherban’, Sergey A. Stel’makh, et al.
Journal of Composites Science (2023) Vol. 7, Iss. 8, pp. 327-327
Open Access | Times Cited: 8
Bridge bottom crack detection and modeling based on faster R‐CNN and BIM
Linfeng Gan, Hu Liu, Yan Yue, et al.
IET Image Processing (2023) Vol. 18, Iss. 3, pp. 664-677
Open Access | Times Cited: 8
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
Accurate Classification of Tunnel Lining Cracks Using Lightweight ShuffleNetV2-1.0-SE Model with DCGAN-Based Data Augmentation and Transfer Learning
Ningyu Zhao, Yi Song, Ailin Yang, et al.
Applied Sciences (2024) Vol. 14, Iss. 10, pp. 4142-4142
Open Access | Times Cited: 1
Utilizing pretrained convolutional neural networks for crack detection and geometric feature recognition in concrete surface images
Miao Su, Jingkai Wan, Qilin Zhou, et al.
Journal of Building Engineering (2024), pp. 111386-111386
Closed Access | Times Cited: 1
Surface Crack Detection using Deep Convolutional Neural Network in Concrete Structures
Alireza Rahai, Mohammad Rahai, Mostafa Iraniparast, et al.
(2022)
Closed Access | Times Cited: 5
A Comprehensive Analysis of the Integration of Deep Learning Models in Concrete Research from a Structural Health Perspective
Ayesha Munira Chowdhury, Rashed Kaiser
Construction Materials (2024) Vol. 4, Iss. 1, pp. 72-90
Open Access
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