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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:

Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques
Omar Abuodeh, Jamal A. Abdalla, Rami A. Hawileh
Composite Structures (2019) Vol. 234, pp. 111698-111698
Closed Access | Times Cited: 119

Showing 1-25 of 119 citing articles:

Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 365
Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices
Arash Teymori Gharah Tapeh, M.Z. Naser
Archives of Computational Methods in Engineering (2022) Vol. 30, Iss. 1, pp. 115-159
Closed Access | Times Cited: 153
Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete
Miao Su, Qingyu Zhong, Hui Peng, et al.
Construction and Building Materials (2020) Vol. 270, pp. 121456-121456
Closed Access | Times Cited: 146
Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
Tadesse G. Wakjira, Mohamed Ibrahim, Usama Ebead, et al.
Engineering Structures (2022) Vol. 255, pp. 113903-113903
Open Access | Times Cited: 122
Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
Farzin Kazemi, Neda Asgarkhani, Torkan Shafighfard, et al.
Archives of Computational Methods in Engineering (2024)
Open Access | Times Cited: 27
Predicting the shear strength of rectangular RC beams strengthened with externally-bonded FRP composites using constrained monotonic neural networks
Ali Benzaamia, Mohamed Ghrici, Redouane Rebouh, et al.
Engineering Structures (2024) Vol. 313, pp. 118192-118192
Closed Access | Times Cited: 25
Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures
H. Tran-Ngoc, Samir Khatir, H. Ho-Khac, et al.
Composite Structures (2020) Vol. 262, pp. 113339-113339
Closed Access | Times Cited: 114
StructuresNet and FireNet: Benchmarking databases and machine learning algorithms in structural and fire engineering domains
M.Z. Naser, Venkatesh Kodur, Huu‐Tai Thai, et al.
Journal of Building Engineering (2021) Vol. 44, pp. 102977-102977
Closed Access | Times Cited: 71
Shear strength prediction of reinforced concrete beams using machine learning
M.S. Sandeep, Koravith Tiprak, Sakdirat Kaewunruen, et al.
Structures (2022) Vol. 47, pp. 1196-1211
Open Access | Times Cited: 57
Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning
Afshin Marani, Moncef L. Nehdi
Engineering Structures (2022) Vol. 257, pp. 114083-114083
Closed Access | Times Cited: 49
Machine learning (ML) based models for predicting the ultimate strength of rectangular concrete-filled steel tube (CFST) columns under eccentric loading
Chen Wang, Tak‐Ming Chan
Engineering Structures (2022) Vol. 276, pp. 115392-115392
Closed Access | Times Cited: 46
Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA
Rana Muhammad Adnan Ikram, Hong‐Liang Dai, Mohammadreza mirshekari chargari, et al.
Measurement (2022) Vol. 205, pp. 112230-112230
Closed Access | Times Cited: 43
Prediction of the frost resistance of high-performance concrete based on RF-REF: A hybrid prediction approach
Xianguo Wu, Shiyi Zheng, Zongbao Feng, et al.
Construction and Building Materials (2022) Vol. 333, pp. 127132-127132
Closed Access | Times Cited: 40
Shear strength prediction of FRP-strengthened concrete beams using interpretable machine learning
Chenxin Wang, Xingxing Zou, Lesley H. Sneed, et al.
Construction and Building Materials (2023) Vol. 407, pp. 133553-133553
Closed Access | Times Cited: 24
Machine learning models for predicting concrete beams shear strength externally bonded with FRP
Jesika Rahman, Palisa Arafin, A. H. M. Muntasir Billah
Structures (2023) Vol. 53, pp. 514-536
Closed Access | Times Cited: 23
Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model
Congcong Fan, Yuanxun Zheng, Shaoqiang Wang, et al.
Construction and Building Materials (2023) Vol. 400, pp. 132602-132602
Closed Access | Times Cited: 21
Intelligent prediction modeling for flexural capacity of FRP-strengthened reinforced concrete beams using machine learning algorithms
Majid Khan, Adil Khan, Asad U. Khan, et al.
Heliyon (2023) Vol. 10, Iss. 1, pp. e23375-e23375
Open Access | Times Cited: 21
Applications of artificial intelligence/machine learning to high-performance composites
Yifeng Wang, Wang Kan, Chuck Zhang
Composites Part B Engineering (2024) Vol. 285, pp. 111740-111740
Closed Access | Times Cited: 12
An efficient improved Gradient Boosting for strain prediction in Near-Surface Mounted fiber-reinforced polymer strengthened reinforced concrete beam
Abdelwahhab Khatir, Roberto Capozucca, Samir Khatir, et al.
Frontiers of Structural and Civil Engineering (2024) Vol. 18, Iss. 8, pp. 1148-1168
Closed Access | Times Cited: 8
Strength models of near-surface mounted (NSM) fibre-reinforced polymer (FRP) shear-strengthened RC beams based on machine learning approaches
Ke Yan, S.S. Zhang, M.J. Jedrzejko, et al.
Composite Structures (2024) Vol. 337, pp. 118045-118045
Closed Access | Times Cited: 7
Shear behavior of RC T-beams externally strengthened with anchored high modulus carbon fiber-reinforced polymer (CFRP) laminates
Haya H. Mhanna, Rami A. Hawileh, Jamal A. Abdalla
Composite Structures (2021) Vol. 272, pp. 114198-114198
Closed Access | Times Cited: 52
Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms
Boğaçhan Başaran, İlker Kalkan, Erhan Bergil, et al.
Composite Structures (2021) Vol. 268, pp. 113972-113972
Closed Access | Times Cited: 51
Identification of the interfacial cohesive law parameters of FRP strips externally bonded to concrete using machine learning techniques
Miao Su, Hui Peng, Ming Yuan, et al.
Engineering Fracture Mechanics (2021) Vol. 247, pp. 107643-107643
Closed Access | Times Cited: 49
Machine-learning-based models to predict shear transfer strength of concrete joints
Tongxu Liu, Zhen Wang, Junlin Zeng, et al.
Engineering Structures (2021) Vol. 249, pp. 113253-113253
Closed Access | Times Cited: 42
Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach
Aman Kumar, Harish Chandra Arora, Krishna Kumar, et al.
Sustainability (2022) Vol. 14, Iss. 2, pp. 845-845
Open Access | Times Cited: 36
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