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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 compressive strength of geopolymer concrete using a hybrid ensemble of grey wolf optimized machine learning estimators
Suraj Kumar Parhi, Sanjaya Kumar Patro
Journal of Building Engineering (2023) Vol. 71, pp. 106521-106521
Closed Access | Times Cited: 74

Showing 1-25 of 74 citing articles:

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives
Nizar Faisal Alkayem, Lei Shen, Ali Mayya, et al.
Journal of Building Engineering (2023) Vol. 83, pp. 108369-108369
Closed Access | Times Cited: 90
Investigation of machine learning models in predicting compressive strength for ultra-high-performance geopolymer concrete: A comparative study
Mohamed Abdellatief, Youssef M. Hassan, Mohamed T. Elnabwy, et al.
Construction and Building Materials (2024) Vol. 436, pp. 136884-136884
Closed Access | Times Cited: 28
AI-driven critical parameter optimization of sustainable self-compacting geopolymer concrete
Suraj Kumar Parhi, Saswat Dwibedy, Saubhagya Kumar Panigrahi
Journal of Building Engineering (2024) Vol. 86, pp. 108923-108923
Closed Access | Times Cited: 23
Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete
Suraj Kumar Parhi, Soumyaranjan Panda, Saswat Dwibedy, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2901-2928
Closed Access | Times Cited: 16
Modelling the compressive strength of geopolymer recycled aggregate concrete using ensemble machine learning
Emadaldin Mohammadi Golafshani, Nima Khodadadi, Tuan Ngo, et al.
Advances in Engineering Software (2024) Vol. 191, pp. 103611-103611
Open Access | Times Cited: 16
Transfer learning framework for modelling the compressive strength of ultra-high performance geopolymer concrete
Ho Anh Thu Nguyen, Duy Hoang Pham, Anh Tuấn Lê, et al.
Construction and Building Materials (2025) Vol. 459, pp. 139746-139746
Closed Access | Times Cited: 1
The critical role of evaluation metrics in handling missing data in machine learning
Ibrahim A. Atoum
International Journal of ADVANCED AND APPLIED SCIENCES (2025) Vol. 12, Iss. 1, pp. 112-124
Open Access | Times Cited: 1
Review on compressive strength and durability of fly-ash-based geopolymers using characterization techniques
Kailash Kumar Singaram, M.A. Khan, Visalakshi Talakokula
Archives of Civil and Mechanical Engineering (2025) Vol. 25, Iss. 2
Closed Access | Times Cited: 1
A comprehensive study on Controlled Low Strength Material
Suraj Kumar Parhi, Saswat Dwibedy, Soumyaranjan Panda, et al.
Journal of Building Engineering (2023) Vol. 76, pp. 107086-107086
Closed Access | Times Cited: 33
Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
Sourav Singh, Sanjaya Kumar Patro, Suraj Kumar Parhi
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3121-3143
Closed Access | Times Cited: 31
Durability, Microstructure, and Optimization of High-Strength Geopolymer Concrete Incorporating Construction and Demolition Waste
Walid E. Elemam, Ahmed M. Tahwia, Mohamed Abdellatief, et al.
Sustainability (2023) Vol. 15, Iss. 22, pp. 15832-15832
Open Access | Times Cited: 31
Alkali–silica reaction expansion prediction in concrete using hybrid metaheuristic optimized machine learning algorithms
Suraj Kumar Parhi, Saubhagya Kumar Panigrahi
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 1091-1113
Closed Access | Times Cited: 30
Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature
Pankaj Kumar Dash, Suraj Kumar Parhi, Sanjaya Kumar Patro, et al.
Construction and Building Materials (2023) Vol. 400, pp. 132814-132814
Closed Access | Times Cited: 29
Influence of chemical constituents of binder and activator in predicting compressive strength of fly ash-based geopolymer concrete using firefly-optimized hybrid ensemble machine learning model
Pankaj Kumar Dash, Suraj Kumar Parhi, Sanjaya Kumar Patro, et al.
Materials Today Communications (2023) Vol. 37, pp. 107485-107485
Closed Access | Times Cited: 26
Production of durable high-strength self-compacting geopolymer concrete with GGBFS as a precursor
Jharana Pradhan, Soumyaranjan Panda, Saswat Dwibedy, et al.
Journal of Material Cycles and Waste Management (2023) Vol. 26, Iss. 1, pp. 529-551
Closed Access | Times Cited: 22
A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete
Shimol Philip, M. Nidhi, Hemn Unis Ahmed
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2555-2583
Closed Access | Times Cited: 11
Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures
Muhammad Faisal Javed, Majid Khan, Moncef L. Nehdi, et al.
Materials Today Communications (2024) Vol. 39, pp. 108832-108832
Closed Access | Times Cited: 10
Explainable machine learning: Compressive strength prediction of FRP-confined concrete column
Tianyu Hu, Hong Zhang, Cheng Cheng, et al.
Materials Today Communications (2024) Vol. 39, pp. 108883-108883
Closed Access | Times Cited: 9
Multi-performance optimization of low-carbon geopolymer considering mechanical, cost, and CO2 emission based on experiment and interpretable learning
Shiqi Wang, Keyu Chen, Jinlong Liu, et al.
Construction and Building Materials (2024) Vol. 425, pp. 136013-136013
Closed Access | Times Cited: 8
Compressive strength prediction of PET fiber-reinforced concrete using Dolphin echolocation optimized decision tree-based machine learning algorithms
Suraj Kumar Parhi, Sanjaya Kumar Patro
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 977-996
Closed Access | Times Cited: 20
GGBFS-Based Self-Compacting Geopolymer Concrete with Optimized Mix Parameters Established on Fresh, Mechanical, and Durability Characteristics
Jharana Pradhan, Soumyaranjan Panda, Suraj Kumar Parhi, et al.
Journal of Materials in Civil Engineering (2023) Vol. 36, Iss. 2
Closed Access | Times Cited: 19
Application of R-curve, ANCOVA, and RSM techniques on fracture toughness enhancement in PET fiber-reinforced concrete
Suraj Kumar Parhi, Sanjaya Kumar Patro
Construction and Building Materials (2023) Vol. 411, pp. 134644-134644
Closed Access | Times Cited: 18
Factors affecting the structural performance of geopolymer concrete beam composites
Saswat Dwibedy, Saubhagya Kumar Panigrahi
Construction and Building Materials (2023) Vol. 409, pp. 134129-134129
Closed Access | Times Cited: 17
Evaluating machine learning algorithms for predicting compressive strength of concrete with mineral admixture using long short-term memory (LSTM) Technique
Abhilash Gogineni, Mrutyunjay Rout, Kumar Shubham
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 2, pp. 1921-1933
Closed Access | Times Cited: 16
Rheological behavior of 3D printed concrete: influential factors and printability prediction scheme
Huaxing Gao, Lang Jin, Yuxuan Chen, et al.
Journal of Building Engineering (2024) Vol. 91, pp. 109626-109626
Open Access | Times Cited: 7
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