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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:

Pavement Crack Width Measurement Based on Laplace's Equation for Continuity and Unambiguity
Wenjuan Wang, Allen Zhang, Kelvin C. P. Wang, et al.
Computer-Aided Civil and Infrastructure Engineering (2017) Vol. 33, Iss. 2, pp. 110-123
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network
Shengyuan Li, Xuefeng Zhao, Guangyi Zhou
Computer-Aided Civil and Infrastructure Engineering (2019) Vol. 34, Iss. 7, pp. 616-634
Closed Access | Times Cited: 400
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V
Yue Fei, Kelvin C. P. Wang, Allen Zhang, et al.
IEEE Transactions on Intelligent Transportation Systems (2019) Vol. 21, Iss. 1, pp. 273-284
Closed Access | Times Cited: 260
Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system
Shang Jiang, Jian Zhang
Computer-Aided Civil and Infrastructure Engineering (2019) Vol. 35, Iss. 6, pp. 549-564
Closed Access | Times Cited: 185
Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique
Shengyuan Li, Xuefeng Zhao
Advances in Civil Engineering (2019) Vol. 2019, Iss. 1
Open Access | Times Cited: 180
Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network
Jianghua Deng, Ye Lü, Vincent C. S. Lee
Computer-Aided Civil and Infrastructure Engineering (2019) Vol. 35, Iss. 4, pp. 373-388
Closed Access | Times Cited: 180
Novel visual crack width measurement based on backbone double-scale features for improved detection automation
Yunchao Tang, Zhaofeng Huang, Zheng Chen, et al.
Engineering Structures (2022) Vol. 274, pp. 115158-115158
Open Access | Times Cited: 146
Crack detection using fusion features‐based broad learning system and image processing
Yang Zhang, Ka‐Veng Yuen
Computer-Aided Civil and Infrastructure Engineering (2021) Vol. 36, Iss. 12, pp. 1568-1584
Closed Access | Times Cited: 104
Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning
Futao Ni, Jian Zhang, Zhiqiang Chen
Computer-Aided Civil and Infrastructure Engineering (2018) Vol. 34, Iss. 5, pp. 367-384
Closed Access | Times Cited: 136
A unified convolutional neural network integrated with conditional random field for pipe defect segmentation
Mingzhu Wang, Jack C.P. Cheng
Computer-Aided Civil and Infrastructure Engineering (2019) Vol. 35, Iss. 2, pp. 162-177
Closed Access | Times Cited: 134
Automatic Pavement Crack Detection and Classification Using Multiscale Feature Attention Network
Song Wei-dong, Guohui Jia, Di Jia, et al.
IEEE Access (2019) Vol. 7, pp. 171001-171012
Open Access | Times Cited: 83
Machine vision-based surface crack analysis for transportation infrastructure
Wenbo Hu, Weidong Wang, Chengbo Ai, et al.
Automation in Construction (2021) Vol. 132, pp. 103973-103973
Closed Access | Times Cited: 82
Real‐time automatic crack detection method based on drone
Shiqiao Meng, Zhiyuan Gao, Ying Zhou, et al.
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 38, Iss. 7, pp. 849-872
Closed Access | Times Cited: 61
Automatic pixel‐level crack detection and evaluation of concrete structures using deep learning
Weijian Zhao, Yunyi Liu, Jiawei Zhang, et al.
Structural Control and Health Monitoring (2022) Vol. 29, Iss. 8
Closed Access | Times Cited: 58
Inspecting Buildings Using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages
Hafiz Suliman Munawar, Fahim Ullah, Amirhossein Heravi, et al.
Drones (2021) Vol. 6, Iss. 1, pp. 5-5
Open Access | Times Cited: 57
Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements
Allen Zhang, Kelvin C. P. Wang, Yang Liu, et al.
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 37, Iss. 13, pp. 1654-1673
Closed Access | Times Cited: 55
Enhancing pavement health assessment: An attention-based approach for accurate crack detection, measurement, and mapping
Eshta Ranyal, Ayan Sadhu, Kamal Jain
Expert Systems with Applications (2024) Vol. 247, pp. 123314-123314
Closed Access | Times Cited: 13
Automatic crack detection and 3D reconstruction of structural appearance using underwater wall-climbing robot
Zhenwei Yu, Yonggang Shen, Yiping Zhang, et al.
Automation in Construction (2024) Vol. 160, pp. 105322-105322
Closed Access | Times Cited: 9
Image processing based automatic recognition of asphalt pavement patch using a metaheuristic optimized machine learning approach
Nhat‐Duc Hoang
Advanced Engineering Informatics (2019) Vol. 40, pp. 110-120
Closed Access | Times Cited: 67
Prediction of the crack condition of highway pavements using machine learning models
Sylvester Inkoom, John Sobanjo, Adrian Barbu, et al.
Structure and Infrastructure Engineering (2019) Vol. 15, Iss. 7, pp. 940-953
Closed Access | Times Cited: 64
Pavement defect detection with fully convolutional network and an uncertainty framework
Tong Zheng, Dongdong Yuan, Jie Gao, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 35, Iss. 8, pp. 832-849
Closed Access | Times Cited: 64
Segment-based pavement crack quantification
Xingxing Weng, Yuchun Huang, Wenzong Wang
Automation in Construction (2019) Vol. 105, pp. 102819-102819
Closed Access | Times Cited: 63
A machine learning approach based on multifractal features for crack assessment of reinforced concrete shells
Apostolos Athanasiou, Arvin Ebrahimkhanlou, Jarrod Zaborac, et al.
Computer-Aided Civil and Infrastructure Engineering (2019) Vol. 35, Iss. 6, pp. 565-578
Closed Access | Times Cited: 58
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
Automated crack assessment and quantitative growth monitoring
Siyu Kong, Jian‐Sheng Fan, Yu‐Fei Liu, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 36, Iss. 5, pp. 656-674
Closed Access | Times Cited: 54
A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference
Jianghua Deng, Ye Lü, Vincent CS Lee
Measurement (2023) Vol. 216, pp. 112892-112892
Closed Access | Times Cited: 20
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