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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:

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: 112

Showing 76-100 of 112 citing articles:

Detection of cracking mechanism transition on reinforced concrete shear walls using graph theory
Pedram Bazrafshan, Arvin Ebrahimkhanlou
(2024), pp. 28-28
Closed Access | Times Cited: 1
CrackScopeNet: A Lightweight Neural Network for Rapid Cracks Detection on Resource-Constrained Drone Platforms
Tao Zhang, Liwei Qin, Quan Zou, et al.
(2024)
Open Access | Times Cited: 1
CrackScopeNet: A Lightweight Neural Network for Rapid Cracks Detection on Resource-Constrained Drone Platforms
Tao Zhang, Liwei Qin, Quan Zou, et al.
(2024)
Open Access | Times Cited: 1
Probabilistic post-earthquake loss measurement for RC framed buildings using crack image analysis
Parnia Zamani, Mohammadjavad Hamidia, Nemat Hassani
Measurement (2024) Vol. 238, pp. 115286-115286
Closed Access | Times Cited: 1
Visual Recognition and Prediction of Crack Propagation Trajectories
Dihao Ai
IntechOpen eBooks (2024)
Closed Access | Times Cited: 1
A General Method for Pre-Flight Preparation in Data Collection for Unmanned Aerial Vehicle-Based Bridge Inspection
Pouya Almasi, Yangjian Xiao, Roshira Premadasa, et al.
Drones (2024) Vol. 8, Iss. 8, pp. 386-386
Open Access | Times Cited: 1
A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
Avik Kumar Das, Christopher K.Y. Leung
Applied Sciences (2024) Vol. 14, Iss. 16, pp. 7194-7194
Open Access | Times Cited: 1
CrackScopeNet: A Lightweight Neural Network for Rapid Crack Detection on Resource-Constrained Drone Platforms
Tao Zhang, Liwei Qin, Quan Zou, et al.
Drones (2024) Vol. 8, Iss. 9, pp. 417-417
Open Access | Times Cited: 1
Make it till you fake it: Construction-centric computational framework for simultaneous image synthetization and multimodal labeling
Ali Tohidifar, Daeho Kim, Sang Hyun Lee
Automation in Construction (2024) Vol. 167, pp. 105696-105696
Open Access | Times Cited: 1
Recognition and quantification of apparent damage to concrete structure based on computer vision
Jiageng Liu, Hongyu Sun, Hongchen Liu, et al.
Measurement (2024) Vol. 240, pp. 115635-115635
Closed Access | Times Cited: 1
Crack analysis of tall concrete wind towers using an ad-hoc deep multiscale encoder–decoder with depth separable convolutions under severely imbalanced data
Jianghua Deng, Linxin Hua, Ye Lü, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 1
Automated vision-based concrete crack measurement system
Nicola Giulietti, Gian Marco Revel, Paolo Chiariotti
Measurement (2024), pp. 115858-115858
Open Access | Times Cited: 1
Dual-encoder network for pavement concrete crack segmentation with multi-stage supervision
Jing Wang, Hong Liang Yao, Jinbin Hu, et al.
Automation in Construction (2024) Vol. 169, pp. 105884-105884
Closed Access | Times Cited: 1
Cycle-consistency-constrained few-shot learning framework for universal multi-type structural damage segmentation
Yunlei Fan, Hui Li, Yuequan Bao, et al.
Structural Health Monitoring (2024)
Closed Access | Times Cited: 1
Automatic Road Pavement Distress Recognition Using Deep Learning Networks from Unmanned Aerial Imagery
Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini, et al.
Drones (2024) Vol. 8, Iss. 6, pp. 244-244
Open Access | Times Cited: 1
Fault Feature Enhancement in Seismic Data Based on Steerable Pyramid Tensor Voting
Xiaoqing Cui, Xuri Huang, Lei Li, et al.
Interpretation (2024) Vol. 12, Iss. 4, pp. T413-T424
Closed Access | Times Cited: 1
Single-stripe-enhanced spacetime stereo reconstruction for concrete defect identification
Linxin Hua, Jianghua Deng, Zhoufeng Shi, et al.
Automation in Construction (2023) Vol. 156, pp. 105136-105136
Open Access | Times Cited: 3
Adaptive acquisition and recognition system of blade surface defects during machining process
Dongbo Wu, Hui Wang, Jiawei Liang, et al.
Measurement (2023) Vol. 225, pp. 114008-114008
Closed Access | Times Cited: 2
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
Photoelectricity Theory-Based Concrete Crack Image Segmentation and Optimal Exposure Interval Research
Haijing Liu, Renwei Luo, Yan Liu, et al.
Applied Sciences (2024) Vol. 14, Iss. 4, pp. 1527-1527
Open Access
From structure health monitoring to forensics: adapting computer vision to support victims of violence
Kiyarash Aminfar, Katherine Scafide, Janusz Wojtusiak, et al.
(2024), pp. 93-93
Closed Access
Component Identification and Depth Estimation for Structural Images Based on Multi-Scale Task Interaction Network
Jianlong Ye, Hongchuan Yu, Gaoyang Liu, et al.
Buildings (2024) Vol. 14, Iss. 4, pp. 983-983
Open Access
A Large-Crack Image-Stitching Method with Cracks as the Regions of Interest
Szu‐Pyng Kao, Jhih-Sian Lin, Fengliang Wang, et al.
Infrastructures (2024) Vol. 9, Iss. 4, pp. 74-74
Open Access
Automatic Detection of Mortar Loss on Masonry Building Facades Based on Deep Learning
Jianxiong Zhang, Hongxing Qiu
(2024)
Closed Access
Deep Learning-Based Method for Detection and Feature Quantification of Microscopic Cracks on the Surface of Concrete Dams
Xiaochun Lu, Qingquan Li
(2024)
Closed Access

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