Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty
Ahmed Rageh, Saeed Eftekhar Azam, Daniel G. Linzell
International Journal of Fatigue (2019) Vol. 134, pp. 105458-105458
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Fatigue modeling using neural networks: A comprehensive review
Jie Chen, Yongming Liu
Fatigue & Fracture of Engineering Materials & Structures (2022) Vol. 45, Iss. 4, pp. 945-979
Open Access | Times Cited: 149
State-of-the-art review on advancements of data mining in structural health monitoring
Meisam Gordan, Saeed-Reza Sabbagh-Yazdi, Zubaidah Ismail, et al.
Measurement (2022) Vol. 193, pp. 110939-110939
Closed Access | Times Cited: 126
Early damage assessment in large-scale structures by innovative statistical pattern recognition methods based on time series modeling and novelty detection
Alireza Entezami, Hashem Shariatmadar, Stefano Mariani
Advances in Engineering Software (2020) Vol. 150, pp. 102923-102923
Closed Access | Times Cited: 72
Intelligent damage diagnosis in bridges using vibration-based monitoring approaches and machine learning: A systematic review
Rosette Niyirora, Wei Ji, Elyse Masengesho, et al.
Results in Engineering (2022) Vol. 16, pp. 100761-100761
Open Access | Times Cited: 61
Advancing Bridge Structural Health Monitoring: Insights into Knowledge-Driven and Data-Driven Approaches
Shuai Wan, Shuhong Guan, Yunchao Tang
Journal of Data Science and Intelligent Systems (2023) Vol. 2, Iss. 3, pp. 129-140
Open Access | Times Cited: 29
Damage detection of bridges subjected to moving load based on domain-adversarial neural network considering measurement and model error
Zhidong Li, Wen-Yu He, Wei‐Xin Ren, et al.
Engineering Structures (2023) Vol. 293, pp. 116601-116601
Closed Access | Times Cited: 17
Machine learning-empowered intelligent vehicle–bridge systems: Current status and future prospects
Jin Zhu, Cheng Wei, Tingpeng Zhang, et al.
Structures (2025) Vol. 74, pp. 108598-108598
Closed Access
A Machine-Learning-Based Approach for Railway Track Monitoring Using Acceleration Measured on an In-Service Train
Abdollah Malekjafarian, Chalres-Antoine Sarrabezolles, Muhammad Arslan Khan, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7568-7568
Open Access | Times Cited: 14
Fatigue crack growth on modified CT specimens using artificial neural networks
Ricardo Baptista, P. Moita, V. Infante
International Journal of Fatigue (2022) Vol. 167, pp. 107357-107357
Closed Access | Times Cited: 20
Fatigue assessment of a complex welded steel bridge connection utilizing a three-dimensional multi-scale finite element model and hotspot stress method
Maryam Mashayekhi, Erin Santini‐Bell
Engineering Structures (2020) Vol. 214, pp. 110624-110624
Open Access | Times Cited: 29
On the use of data-driven machine learning for remaining life estimation of metallic materials based on Ye-Wang damage theory
Lei Gan, Hao Wu, Zheng Zhong
International Journal of Fatigue (2021) Vol. 156, pp. 106666-106666
Closed Access | Times Cited: 24
Indirect Monitoring of Frequencies of a Multiple Span Bridge Using Data Collected from an Instrumented Train: A Field Case Study
Abdollah Malekjafarian, Muhammad Arslan Khan, Eugene J. OBrien, et al.
Sensors (2022) Vol. 22, Iss. 19, pp. 7468-7468
Open Access | Times Cited: 18
Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
Samira Ardani, Saeed Eftekhar Azam, Daniel G. Linzell
Applied Sciences (2023) Vol. 13, Iss. 3, pp. 1935-1935
Open Access | Times Cited: 10
A non-intrusive dynamic sensitivity-based substructure model updating method for nonlinear systems
Zhifu Cao, Haibo Wei, Daosen Liang, et al.
International Journal of Mechanical Sciences (2023) Vol. 248, pp. 108218-108218
Closed Access | Times Cited: 9
Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics
Yun Long, Xi’an Guo, Tianbai Xiao
Symmetry (2024) Vol. 16, Iss. 2, pp. 155-155
Open Access | Times Cited: 3
Unsupervised Machine Learning for Robust Bridge Damage Detection: Full-Scale Experimental Validation
Emmanuel Akintunde, Saeed Eftekhar Azam, Ahmed Rageh, et al.
Engineering Structures (2021) Vol. 249, pp. 113250-113250
Open Access | Times Cited: 19
A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method
Chao Feng, Lianyong Xu, Lei Zhao, et al.
Advanced Engineering Materials (2023) Vol. 25, Iss. 9
Closed Access | Times Cited: 7
Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review
Bin Xu, Liwen Zhang, Weibin Zhang, et al.
Journal of Marine Science and Engineering (2024) Vol. 12, Iss. 5, pp. 813-813
Open Access | Times Cited: 2
Service Life Evaluation of Curved Intercity Rail Bridges Based on Fatigue Failure
Hongwei Zhang, Shaolin Chen, Wei Zhang, et al.
Infrastructures (2024) Vol. 9, Iss. 9, pp. 139-139
Open Access | Times Cited: 2
State-of-the-art of fatigue performance and estimation approach of orthotropic steel bridge decks
Hai Bo Yang, Ping Wang, Özler Karakaş, et al.
Structures (2024) Vol. 70, pp. 107729-107729
Closed Access | Times Cited: 2
A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation
Thomas Fleet, Khangamlung Kamei, Feiyang He, et al.
Sensors (2020) Vol. 20, Iss. 23, pp. 6847-6847
Open Access | Times Cited: 19
Assessing the fatigue damage of concrete structures using automatically classified crack severity level information
Hongshuo Sun, Li Song, Zhiwu Yu
International Journal of Fatigue (2023) Vol. 179, pp. 108071-108071
Closed Access | Times Cited: 6
Fatigue crack detection in welded structural components of steel bridges using artificial neural network
Maryam Mashayekhi, Erin Santini‐Bell, Saeed Eftekhar Azam
Journal of Civil Structural Health Monitoring (2021) Vol. 11, Iss. 4, pp. 931-947
Closed Access | Times Cited: 15
Model updating of nonlinear structures using substructuring method
Wei Tian, Shun Weng, Yong Xia
Journal of Sound and Vibration (2021) Vol. 521, pp. 116719-116719
Closed Access | Times Cited: 15
Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges
Samira Ardani, Emmanuel Akintunde, Daniel G. Linzell, et al.
Engineering Structures (2023) Vol. 286, pp. 116096-116096
Open Access | Times Cited: 5
Page 1 - Next Page

Scroll to top