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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:

Visual structural inspection datasets
Eric Bianchi, Matthew H. Hebdon
Automation in Construction (2022) Vol. 139, pp. 104299-104299
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks
Chenqin Xiong, Tarek Zayed, Eslam Mohammed Abdelkader
Construction and Building Materials (2024) Vol. 414, pp. 135025-135025
Closed Access | Times Cited: 43
Image-Based Structural Health Monitoring: A Systematic Review
John Mark G. Payawal, Dong Keon Kim
Applied Sciences (2023) Vol. 13, Iss. 2, pp. 968-968
Open Access | Times Cited: 27
Real-time instance-level detection of asphalt pavement distress combining space-to-depth (SPD) YOLO and omni-scale network (OSNet)
Jiale Li, Chenglong Yuan, Xuefei Wang
Automation in Construction (2023) Vol. 155, pp. 105062-105062
Closed Access | Times Cited: 24
Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection
Chenyu Zhang, Zhaozheng Yin, Ruwen Qin
Automation in Construction (2024) Vol. 159, pp. 105292-105292
Open Access | Times Cited: 8
From classification to segmentation with explainable AI: A study on crack detection and growth monitoring
Florent Forest, Hugo Porta, Devis Tuia, et al.
Automation in Construction (2024) Vol. 165, pp. 105497-105497
Open Access | Times Cited: 6
Self‐training approach for crack detection using synthesized crack images based on conditional generative adversarial network
Seungbo Shim
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 7, pp. 1019-1041
Open Access | Times Cited: 15
Dataset for developing deep learning models to assess crack width and self-healing progress in concrete
Jacek Jakubowski, Kamil Tomczak
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Few-Shot Learning Augmented with Image Transformation for Multiclass Structural Damage Classification
Kyle Dunphy, Madhushan Buwaneswaran, Katarina Grolinger, et al.
Journal of Computing in Civil Engineering (2025) Vol. 39, Iss. 3
Closed Access
Post-earthquake structural damage detection with tunable semi-synthetic image generation
Piercarlo Dondi, Alessio Gullotti, Michele Inchingolo, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 147, pp. 110302-110302
Open Access
Fully Automatic Surface Defect Detection of CFRP Using Computer Vision and an Augmented YOLOv8 Model
Keyu Chen, Lin Jun, B. You, et al.
Journal of Performance of Constructed Facilities (2025) Vol. 39, Iss. 3
Closed Access
Semi-supervised method for automated detection and quantitative assessment of corrosion states in structural members
Yonghui An, Lingxue Kong, Chuan-Chuan Hou, et al.
Automation in Construction (2025) Vol. 174, pp. 106155-106155
Closed Access
Automatic compliance inspection and monitoring of building structural members using multi-temporal point clouds
Kaveh Mirzaei, Mehrdad Arashpour, Ehsan Asadi, et al.
Journal of Building Engineering (2023) Vol. 72, pp. 106570-106570
Open Access | Times Cited: 12
A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images
Chenyu Zhang, Muhammad Monjurul Karim, Ruwen Qin
Transportation Research Record Journal of the Transportation Research Board (2023) Vol. 2677, Iss. 7, pp. 693-704
Open Access | Times Cited: 11
Random bridge generator as a platform for developing computer vision-based structural inspection algorithms
Haojia Cheng, Wenhao Chai, Jiabao Hu, et al.
Journal of Infrastructure Intelligence and Resilience (2024) Vol. 3, Iss. 2, pp. 100098-100098
Open Access | Times Cited: 4
Automatic PAUT crack detection and depth identification framework based on inspection robot and deep learning method
Fei Hu, Hongye Gou, Haozhe Yang, et al.
Journal of Infrastructure Intelligence and Resilience (2024) Vol. 4, Iss. 1, pp. 100113-100113
Closed Access | Times Cited: 3
Application of self-sensing concrete sensors for bridge monitoring − A review of recent developments, challenges, and future prospects
Mohammad Siahkouhi, Maria Rashidi, Fidelis Mashiri, et al.
Measurement (2024), pp. 116543-116543
Closed Access | Times Cited: 3
Unsupervised Domain Adaptation Approach for Vision-Based Semantic Understanding of Bridge Inspection Scenes without Manual Annotations
Yasutaka Narazaki, W. C. Pang, Gaoang Wang, et al.
Journal of Bridge Engineering (2023) Vol. 29, Iss. 2
Closed Access | Times Cited: 7
A pre-failure narrow concrete cracks dataset for engineering structures damage classification and segmentation
Karolina Tomaszkiewicz, Tomasz Owerko
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 5
Auscultation Techniques of Constituent Materials of Monuments and Ancient Constructions: Immediate Techniques and Instrumental Techniques

Journal of Materials and Polymer Science (2022)
Open Access | Times Cited: 8
Segmentation of Bridge Components from Various Real Scene Inspection Images
Shengyi Wang, Nora El-Gohary
Construction Research Congress 2022 (2024), pp. 259-268
Closed Access | Times Cited: 1
A comparison of learning-based approaches for the corrosion detection on barrels in industrial applications
Dennis Haitz, Patrick Hübner, Markus Ulrich, et al.
tm - Technisches Messen (2023) Vol. 90, Iss. 7-8, pp. 522-532
Closed Access | Times Cited: 2
Automatisierte Erfassung von Schäden in der Brückenprüfung mithilfe maschineller Lernverfahren
Firdes Çelik, Markus König
Springer eBooks (2024), pp. 327-339
Closed Access
Surface Damage Detection and Localization for Bridge Visual Inspection Based on Deep Learning and 3D Reconstruction
Youhao Ni, Jianxiao Mao, Hao Wang, et al.
Structural Control and Health Monitoring (2024) Vol. 2024, Iss. 1
Open Access
Weakly‐supervised structural component segmentation via scribble annotations
Chenyu Zhang, Ke Li, Zhaozheng Yin, et al.
Computer-Aided Civil and Infrastructure Engineering (2024)
Closed Access
BD3: Building Defects Detection Dataset for Benchmarking Computer Vision Techniques for Automated Defect Identification
Praveen Kottari, Pandarasamy Arjunan
(2024), pp. 297-301
Open Access
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