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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:

Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods
Yaser Moodi, Mohammad Ghasemi, Seyed Roohollah Mousavi
Journal of Reinforced Plastics and Composites (2021) Vol. 41, Iss. 3-4, pp. 130-146
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
Ibomoiye Domor Mienye, Yanxia Sun
IEEE Access (2023) Vol. 11, pp. 30628-30638
Open Access | Times Cited: 67
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: 30
Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions
Ibomoiye Domor Mienye, Nobert Jere
IEEE Access (2024) Vol. 12, pp. 96893-96910
Open Access | Times Cited: 20
Prediction of agricultural drought index in a hot and dry climate using advanced hybrid machine learning
Mohsen Rezaei, Mehdi Azhdary Moghaddam, Gholamreza Azizyan, et al.
Ain Shams Engineering Journal (2024) Vol. 15, Iss. 5, pp. 102686-102686
Open Access | Times Cited: 7
Machine-learning-based models versus design-oriented models for predicting the axial compressive load of FRP-confined rectangular RC columns
Yehia A.K. Sayed, Alzhraa A. Ibrahim, Ashot Tamrazyan, et al.
Engineering Structures (2023) Vol. 285, pp. 116030-116030
Closed Access | Times Cited: 18
XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete
Jiang Bian, Ruili Huo, Yan Zhong, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 4, pp. 1423-1439
Closed Access | Times Cited: 6
Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm
Ping Li, Yanru Zhang, Jiming Gu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
GNDO-SVR: An efficient surrogate modeling approach for reliability-based design optimization of concrete dams
Mohammad Javad Ghasemi Rad, Sima Ohadi, Jafar Jafari‐Asl, et al.
Structures (2021) Vol. 35, pp. 722-733
Closed Access | Times Cited: 28
A BFRC compressive strength prediction method via kernel extreme learning machine-genetic algorithm
Hong Li, Jiajian Lin, Dawei Zhao, et al.
Construction and Building Materials (2022) Vol. 344, pp. 128076-128076
Open Access | Times Cited: 14
Support Vector Machine to Predict the Pile Settlement using Novel Optimization Algorithm
Qingyun Ge, Caimei Li, Fulian Yang
Geotechnical and Geological Engineering (2023) Vol. 41, Iss. 7, pp. 3861-3875
Closed Access | Times Cited: 8
A new formulation for predicting the ultimate capacities of FRP-confined concrete using advanced machine learning framework: Developed structural reliability analysis
Mahmoud Alfouneh, Mohamed El Amine Ben Seghier, Behrooz Keshtegar, et al.
Structures (2024) Vol. 68, pp. 107158-107158
Closed Access | Times Cited: 2
Predicting the Ultimate and Relative Bond Strength of Corroded Bars and Surrounding Concrete by Considering the Effect of Transverse Rebar Using Machine Learning
Mohammad Mousavi, Abdolhamid Bahr Peyma, Seyed Roohollah Mousavi, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2022) Vol. 47, Iss. 1, pp. 193-219
Closed Access | Times Cited: 11
Axial compressive behavior of GFRP-timber-reinforced concrete composite columns
Fubin Zhang, Hu Luo, Jianzhuang Xiao, et al.
Low-carbon Materials and Green Construction (2023) Vol. 1, Iss. 1
Open Access | Times Cited: 6
Evaluation of compressive strength of the HPC produced with admixtures by a novel hybrid SVR model
Xiaoxuan Hu
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 6, Iss. 3, pp. 357-370
Closed Access | Times Cited: 6
Prediction of compressive strength of concrete based on IABC-MLP algorithm
Ping Li, Yanru Zhang, Jiming Gu, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Investigation of mechanical properties of high-performance concrete via optimized neural network approaches
Xuyang Wang, Rijie Cong
Journal of Engineering and Applied Science (2024) Vol. 71, Iss. 1
Open Access | Times Cited: 1
Prediction of high-performance concrete compressive strength through novel structured neural network
Huan Li
Journal of Intelligent & Fuzzy Systems (2023) Vol. 45, Iss. 1, pp. 1791-1803
Closed Access | Times Cited: 3
Estimation of compressive strength and slump of HPC concrete using neural network coupling with metaheuristic algorithms
Wenqiao Li, Ruijie Wang, Qisheng Ai, et al.
Journal of Intelligent & Fuzzy Systems (2023) Vol. 45, Iss. 1, pp. 577-591
Closed Access | Times Cited: 3
Research on multi-model prediction of skeleton curves of prefabricated concrete columns based on Residual fusion Long Short-Term Memory -Transformer
Wang-Xi Zhang, Baoqi Yan, Wei‐Jian Yi
Journal of Building Engineering (2023) Vol. 79, pp. 107821-107821
Closed Access | Times Cited: 3
The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering
Mohammad Alhusban, Mohannad Alhusban, Ayah A. Alkhawaldeh
Sustainability (2023) Vol. 16, Iss. 1, pp. 11-11
Open Access | Times Cited: 3
Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete
Xiaoyan Yan, Shasha Lu
Structural Concrete (2022) Vol. 24, Iss. 3, pp. 4047-4063
Closed Access | Times Cited: 5
Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches
Peng Weng, Jingjing Xie, Yang Zou
Journal of Intelligent & Fuzzy Systems (2023) Vol. 44, Iss. 5, pp. 8711-8724
Closed Access | Times Cited: 2
Employing a support vector regression technique coupled with optimizers to estimate the compressive strength of reinforced concrete connections
Hongmei Yao
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 4503-4518
Closed Access
An improved prediction of high-performance concrete compressive strength using ensemble models and neural networks
Umar Jibrin Muhammad, Ismail I Aminu, Ismail Aminu Mahmoud, et al.
AI in Civil Engineering (2024) Vol. 3, Iss. 1
Open Access
Auto‐regulated radial base function structure implementation in the hybrid and ensemble hybrid domains to assess the hardened properties of novel mixture high performance concrete
Lei Yang
Structural Concrete (2024) Vol. 25, Iss. 5, pp. 3804-3823
Closed Access
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