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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:

A Bayesian Probabilistic Approach for Acoustic Emission‐Based Rail Condition Assessment
Junfang Wang, Xiao Zhou Liu, Yi‐Qing Ni
Computer-Aided Civil and Infrastructure Engineering (2017) Vol. 33, Iss. 1, pp. 21-34
Closed Access | Times Cited: 79

Showing 1-25 of 79 citing articles:

In-situ synthesizing carbon nanotubes on cement to develop self-sensing cementitious composites for smart high-speed rail infrastructures
Siqi Ding, Yu Xiang, Yi‐Qing Ni, et al.
Nano Today (2022) Vol. 43, pp. 101438-101438
Closed Access | Times Cited: 205
Bayesian multi-task learning methodology for reconstruction of structural health monitoring data
Hua‐Ping Wan, Yi‐Qing Ni
Structural Health Monitoring (2018) Vol. 18, Iss. 4, pp. 1282-1309
Open Access | Times Cited: 185
Real‐time regional seismic damage assessment framework based on long short‐term memory neural network
Yongjia Xu, Xinzheng Lu, Barbaros Çetiner, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 36, Iss. 4, pp. 504-521
Closed Access | Times Cited: 137
A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data
Yi‐Qing Ni, You‐Wu Wang, Chaodong Zhang
Engineering Structures (2020) Vol. 212, pp. 110520-110520
Open Access | Times Cited: 124
Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
Hua‐Ping Wan, Yi‐Qing Ni
Journal of Structural Engineering (2018) Vol. 144, Iss. 9
Open Access | Times Cited: 119
Integrating structural control, health monitoring, and energy harvesting for smart cities
Sajad Javadinasab Hormozabad, Mariantonieta Gutiérrez Soto, Hojjat Adeli
Expert Systems (2021) Vol. 38, Iss. 8
Open Access | Times Cited: 93
Acoustic emission wave classification for rail crack monitoring based on synchrosqueezed wavelet transform and multi-branch convolutional neural network
Dan Li, Yang Wang, Wang‐Ji Yan, et al.
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 1563-1582
Closed Access | Times Cited: 80
Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme
Qiang Wang, Yang Dai, Zhanguo Ma, et al.
Structural Control and Health Monitoring (2022) Vol. 29, Iss. 11
Closed Access | Times Cited: 37
Intelligent recognition of defects in high‐speed railway slab track with limited dataset
Xiaopei Cai, Xueyang Tang, Shuo Pan, et al.
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 6, pp. 911-928
Open Access | Times Cited: 31
Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability
Qiang Wang, Yang Dai, Zhanguo Ma, et al.
Structural Health Monitoring (2023) Vol. 23, Iss. 1, pp. 588-604
Closed Access | Times Cited: 27
Revamping structural health monitoring of advanced rail transit systems: A paradigmatic shift from digital shadows to digital twins
Mujib Olamide Adeagbo, Sumei Wang, Yi‐Qing Ni
Advanced Engineering Informatics (2024) Vol. 61, pp. 102450-102450
Open Access | Times Cited: 12
Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data
Ying Zhou, Chenshuang Li, Cheng Zhou, et al.
Reliability Engineering & System Safety (2018) Vol. 180, pp. 152-167
Closed Access | Times Cited: 78
Combining association rules mining with complex networks to monitor coupled risks
Ying Zhou, Chenshuang Li, Lieyun Ding, et al.
Reliability Engineering & System Safety (2019) Vol. 186, pp. 194-208
Closed Access | Times Cited: 72
An acoustic-homologous transfer learning approach for acoustic emission–based rail condition evaluation
Si‐Xin Chen, Lu Zhou, Yi‐Qing Ni, et al.
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 2161-2181
Open Access | Times Cited: 55
Guided wave–based rail flaw detection technologies: state-of-the-art review
Hao Ge, David Chua Kim Huat, C. G. Koh, et al.
Structural Health Monitoring (2021) Vol. 21, Iss. 3, pp. 1287-1308
Closed Access | Times Cited: 54
Practical model updating of the Ting Kau Bridge through the MCMC-based Bayesian algorithm utilizing measured modal parameters
Chen Fang, Hongjun Liu, Heung‐Fai Lam, et al.
Engineering Structures (2022) Vol. 254, pp. 113839-113839
Closed Access | Times Cited: 33
Damage identification using piezoelectric electromechanical Impedance: A brief review from a numerical framework perspective
Pei Cao, Shengli Zhang, Zequn Wang, et al.
Structures (2023) Vol. 50, pp. 1906-1921
Open Access | Times Cited: 19
Study on acoustic emission properties and crack growth rate identification of rail steels under different fatigue loading conditions
Jingmang Xu, Kai Wang, Qiantao Ma, et al.
International Journal of Fatigue (2023) Vol. 172, pp. 107638-107638
Closed Access | Times Cited: 18
Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey
Kai Zhou, Zequn Wang, Yi-Qing Ni, et al.
Journal of Infrastructure Intelligence and Resilience (2023) Vol. 2, Iss. 2, pp. 100031-100031
Open Access | Times Cited: 17
Hybrid Pixel‐Level Crack Segmentation for Ballastless Track Slab Using Digital Twin Model and Weakly Supervised Style Transfer
Wenbo Hu, Weidong Wang, Xianhua Liu, et al.
Structural Control and Health Monitoring (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 5
Acoustic emission Bayesian source location: Onset time challenge
Ramin Madarshahian, Paul Ziehl, Juan M. Caicedo
Mechanical Systems and Signal Processing (2019) Vol. 123, pp. 483-495
Open Access | Times Cited: 53
Diffuse ultrasonic wave-based structural health monitoring for railway turnouts
Kai Wang, Wuxiong Cao, Lei Xu, et al.
Ultrasonics (2019) Vol. 101, pp. 106031-106031
Closed Access | Times Cited: 47
A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring
Yi‐Qing Ni, Qiu-Hu Zhang
Structural Health Monitoring (2020) Vol. 20, Iss. 4, pp. 1536-1550
Open Access | Times Cited: 42
A parameter optimized variational mode decomposition method for rail crack detection based on acoustic emission technique
Xin Zhang, Tiantian Sun, Yan Wang, et al.
Nondestructive Testing And Evaluation (2020), pp. 1-29
Closed Access | Times Cited: 41
Predicting fatigue damage of highway suspension bridge hangers using weigh-in-motion data and machine learning
Yang Deng, Meng Zhang, Dongming Feng, et al.
Structure and Infrastructure Engineering (2020) Vol. 17, Iss. 2, pp. 233-248
Closed Access | Times Cited: 40
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