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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 recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network
Nhat‐Duc Hoang, Quoc-Lam Nguyen, Van-Duc Tran
Automation in Construction (2018) Vol. 94, pp. 203-213
Closed Access | Times Cited: 299

Showing 1-25 of 299 citing articles:

Roles of artificial intelligence in construction engineering and management: A critical review and future trends
Yue Pan, Limao Zhang
Automation in Construction (2020) Vol. 122, pp. 103517-103517
Closed Access | Times Cited: 741
A review of computer vision–based structural health monitoring at local and global levels
Chuan‐Zhi Dong, F. Necati Çatbaş
Structural Health Monitoring (2020) Vol. 20, Iss. 2, pp. 692-743
Closed Access | Times Cited: 523
Image-based concrete crack detection in tunnels using deep fully convolutional networks
Yupeng Ren, Jisheng Huang, Zhiyou Hong, et al.
Construction and Building Materials (2019) Vol. 234, pp. 117367-117367
Closed Access | Times Cited: 375
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
Dieu Tien Bui, Nhat‐Duc Hoang, Francisco Martínez–Álvarez, et al.
The Science of The Total Environment (2019) Vol. 701, pp. 134413-134413
Closed Access | Times Cited: 325
Automated pavement crack detection and segmentation based on two‐step convolutional neural network
Jingwei Liu, Xu Yang, Stephen Lau, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 35, Iss. 11, pp. 1291-1305
Closed Access | Times Cited: 289
CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection
Ju Huyan, Wei Li, Susan Tighe, et al.
Structural Control and Health Monitoring (2020) Vol. 27, Iss. 8
Open Access | Times Cited: 257
A critical review and comparative study on image segmentation-based techniques for pavement crack detection
Narges Kheradmandi, Vida Mehranfar
Construction and Building Materials (2022) Vol. 321, pp. 126162-126162
Closed Access | Times Cited: 234
An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement
Ankang Ji, Xiaolong Xue, Yuna Wang, et al.
Automation in Construction (2020) Vol. 114, pp. 103176-103176
Closed Access | Times Cited: 205
A deep learning-based framework for an automated defect detection system for sewer pipes
Xianfei Yin, Yuan Chen, Ahmed Bouferguène, et al.
Automation in Construction (2019) Vol. 109, pp. 102967-102967
Closed Access | Times Cited: 201
A cost effective solution for pavement crack inspection using cameras and deep neural networks
Qipei Mei, Mustafa Gül
Construction and Building Materials (2020) Vol. 256, pp. 119397-119397
Closed Access | Times Cited: 187
Deep learning-based road damage detection and classification for multiple countries
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, et al.
Automation in Construction (2021) Vol. 132, pp. 103935-103935
Open Access | Times Cited: 178
A real-time detection approach for bridge cracks based on YOLOv4-FPM
Zhenwei Yu, Yonggang Shen, Chenkai Shen
Automation in Construction (2020) Vol. 122, pp. 103514-103514
Closed Access | Times Cited: 168
CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning
Kaige Zhang, Yingtao Zhang, Heng-Da Cheng
IEEE Transactions on Intelligent Transportation Systems (2020) Vol. 22, Iss. 2, pp. 1306-1319
Open Access | Times Cited: 152
DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism
Arunabha M. Roy, Jayabrata Bhaduri
Advanced Engineering Informatics (2023) Vol. 56, pp. 102007-102007
Closed Access | Times Cited: 152
Pavement crack detection based on transformer network
Feng Guo, Yu Qian, Jian Liu, et al.
Automation in Construction (2022) Vol. 145, pp. 104646-104646
Closed Access | Times Cited: 126
Machine learning techniques for pavement condition evaluation
Nima Sholevar, Amir Golroo, Sahand Roghani Esfahani
Automation in Construction (2022) Vol. 136, pp. 104190-104190
Closed Access | Times Cited: 122
Classification and analysis of deep learning applications in construction: A systematic literature review
Rana Khallaf, Mohamed Khallaf
Automation in Construction (2021) Vol. 129, pp. 103760-103760
Closed Access | Times Cited: 120
Automated extraction and evaluation of fracture trace maps from rock tunnel face images via deep learning
Jiayao Chen, Mingliang Zhou, Hongwei Huang, et al.
International Journal of Rock Mechanics and Mining Sciences (2021) Vol. 142, pp. 104745-104745
Closed Access | Times Cited: 112
Review on computer vision-based crack detection and quantification methodologies for civil structures
Jianghua Deng, Amardeep Singh, Yiyi Zhou, et al.
Construction and Building Materials (2022) Vol. 356, pp. 129238-129238
Closed Access | Times Cited: 108
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
Younes Hamishebahar, Hong Guan, Stephen So, et al.
Applied Sciences (2022) Vol. 12, Iss. 3, pp. 1374-1374
Open Access | Times Cited: 92
Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion
Guanting Ye, Sai Li, Manxu Zhou, et al.
Automation in Construction (2024) Vol. 160, pp. 105331-105331
Closed Access | Times Cited: 19
CNN-Transformer hybrid network for concrete dam crack patrol inspection
Mingchao Li, Jingyue Yuan, Qiubing Ren, et al.
Automation in Construction (2024) Vol. 163, pp. 105440-105440
Closed Access | Times Cited: 18
Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning
Chuncheng Feng, Hua Zhang, Shuang Wang, et al.
KSCE Journal of Civil Engineering (2019) Vol. 23, Iss. 10, pp. 4493-4502
Closed Access | Times Cited: 137
Increasing the robustness of material-specific deep learning models for crack detection across different materials
Mohamad Alipour, Devin K. Harris
Engineering Structures (2020) Vol. 206, pp. 110157-110157
Closed Access | Times Cited: 124
Improving progress monitoring by fusing point clouds, semantic data and computer vision
Alexander Braun, Sebastian Tuttas, André Borrmann, et al.
Automation in Construction (2020) Vol. 116, pp. 103210-103210
Open Access | Times Cited: 120
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