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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:

Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
Farzin Kazemi, Torkan Shafighfard, Doo‐Yeol Yoo
Archives of Computational Methods in Engineering (2024) Vol. 31, Iss. 4, pp. 2049-2078
Closed Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 109053-109053
Closed Access | Times Cited: 47
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
Torkan Shafighfard, Farzin Kazemi, Faramarz Bagherzadeh, et al.
Computer-Aided Civil and Infrastructure Engineering (2024) Vol. 39, Iss. 23, pp. 3573-3594
Open Access | Times Cited: 43
Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
Farzin Kazemi, Neda Asgarkhani, Robert Jankowski
Expert Systems with Applications (2024) Vol. 255, pp. 124897-124897
Closed Access | Times Cited: 37
Development of novel concave and convex trowels for higher interlayer strength of 3D printed cement paste
Lewei He, Jiahui Pan, Yu Sheng Hee, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03745-e03745
Open Access | Times Cited: 24
Durability and ecological assessment of low-carbon high-strength concrete with short AR-glass fibers: Effects of high-volume of solid waste materials
Ahmed M. Tahwia, Abdelrahman k. Elmansy, Mohamed Abdellatief, et al.
Construction and Building Materials (2024) Vol. 429, pp. 136422-136422
Closed Access | Times Cited: 21
Grey wolf optimizer integrated within boosting algorithm: Application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes
Aybike Özyüksel Çiftçioğlu, Farzin Kazemi, Torkan Shafighfard
Applied Materials Today (2025) Vol. 42, pp. 102601-102601
Closed Access | Times Cited: 1
Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete
Farzin Kazemi, Torkan Shafighfard, Robert Jankowski, et al.
Archives of Civil and Mechanical Engineering (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 9
Optimizing compressive strength of quaternary-blended cement concrete through ensemble-instance-based machine learning
Ammar Babiker, Yassir M. Abbas, M. Iqbal Khan, et al.
Materials Today Communications (2024) Vol. 39, pp. 109150-109150
Closed Access | Times Cited: 5
Experimental and Mesoscopic Simulation Study of Size Effect on Splitting Tensile Performance of Spontaneous-combustion Coal Gangue Concrete
Jiaqi Jiang, Haiqing Liu, Jinli Wang
Journal of Building Engineering (2024) Vol. 92, pp. 109677-109677
Closed Access | Times Cited: 5
Proposal of a sequential machine learning modelling approach for optimal cementitious composites
Elyas Asadi Shamsabadi, Saeed Mohammadzadeh Chianeh, Daniel Dias‐da‐Costa
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 109837-109837
Open Access
Robust graph contrastive learning with multi-hop views for node classification
Yutong Wang, Junheng Zhang, Rui Cao, et al.
Applied Soft Computing (2025), pp. 112783-112783
Closed Access
RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials
Farzin Kazemi, Aybike Özyüksel Çiftçioğlu, Torkan Shafighfard, et al.
Computers & Structures (2025) Vol. 308, pp. 107657-107657
Closed Access
A critical analysis of compressive strength prediction of glass fiber and carbon fiber reinforced concrete over machine learning models
K. K. Yaswanth, V. S. Vani, Krupasindhu Biswal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Closed Access
An explainable boosting-based ensemble machine learning model for predicting the properties of date palm fiber reinforced concrete
Musa Adamu, Sanjog Chhetri Sapkota, Sourav Das, et al.
Sustainable Chemistry and Pharmacy (2025) Vol. 44, pp. 101949-101949
Closed Access
A hybrid machine learning approach for predicting fiber-reinforced polymer-concrete interface bond strength
Sarmed Wahab, Babatunde Abiodun Salami, Hassan Danish, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110458-110458
Closed Access
Multi-optimization for thermal deformation of gravitational wave telescope based on CFRP characteristics
Bohong Li, Yong Nan Yan, Jian Luo, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment
Boyan Xu, Ching Kwek Pooi, Tsuey Shan Yeap, et al.
Journal of Water Process Engineering (2024) Vol. 65, pp. 105888-105888
Closed Access | Times Cited: 3
AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering
Metin Katlav, Mehmet Emin Tabar, Kâzım Türk
Materials Today Communications (2024) Vol. 42, pp. 111286-111286
Closed Access | Times Cited: 3
Assessment of Fiber Corrosion Influence in the Flexural Performance of Steel Fiber-Reinforced Concrete
Mauro Fernandes, R. Neves
Applied Sciences (2024) Vol. 14, Iss. 13, pp. 5611-5611
Open Access | Times Cited: 2
Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
Neda Asgarkhani, Farzin Kazemi, Robert Jankowski
Lecture notes in computer science (2024), pp. 470-478
Closed Access | Times Cited: 2
Comprehensive review of AI and ML tools for earthquake damage assessment and retrofitting strategies
P. K. S. Bhadauria
Earth Science Informatics (2024) Vol. 17, Iss. 5, pp. 3945-3962
Closed Access | Times Cited: 2
Predictions of Mechanical Properties of Fiber Reinforced Concrete using Ensemble Learning Models
Ningyue Su, Shuaicheng Guo, Caijun Shi, et al.
Journal of Building Engineering (2024) Vol. 98, pp. 110990-110990
Closed Access | Times Cited: 2
A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability
Kai Wu, Sihao Zhou, Qiang Li, et al.
Materials Today Communications (2024), pp. 110667-110667
Closed Access | Times Cited: 2
Effect of Discarded Geotextile Fiber on Mechanical Properties and Durability of Cement-Stabilized Diabase Waste Residue
Chengwei Zhang, Changbo Du, Fu Yi, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024)
Closed Access | Times Cited: 1
An end-to-end framework for fire following earthquake simulation at regional scale: A case study on the 2024 Japan Noto Peninsula earthquake
Shandy Rianto, Xinzheng Lu, Donglian Gu, et al.
International Journal of Disaster Risk Reduction (2024), pp. 104859-104859
Closed Access | Times Cited: 1
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