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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 Fly-Ash-Based Concrete Using Ensemble and Non-Ensemble Supervised Machine-Learning Approaches
Yang Song, Jun Zhao, Krzysztof Adam Ostrowski, et al.
Applied Sciences (2021) Vol. 12, Iss. 1, pp. 361-361
Open Access | Times Cited: 55

Showing 26-50 of 55 citing articles:

Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework
Geraldo F. Neto, Bruno da Silva Macêdo, Tales Humberto de Aquino Boratto, et al.
Mathematical and Computational Applications (2025) Vol. 30, Iss. 1, pp. 16-16
Open Access
Ensemble machine learning models for predicting concrete compressive strength incorporating various sand types
Rupesh Kumar Tipu, Shweta Bansal, Vandna Batra, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 4
Closed Access
Prediction of ultimate strength for high strength concrete (HSC) using machine learning approaches - optimized by PSO technique
P. Ruba, S. Aarthi, P. Bhuvaneshwari
Deleted Journal (2025) Vol. 28, Iss. 1
Open Access
Machine learning approach in fused filament fabrication for flexural characteristics of polylactic acid reinforced with carbon fibres
Akash Jain, Kanishka Pathik, Saloni Upadhyay, et al.
Progress in Additive Manufacturing (2025)
Closed Access
Prediction of compressive strength of blended concrete with Alccofine and GGBFS by applying ensemble machine learning algorithms
A. Punitha, C. Vivek Kumar, R. Swetha, et al.
Journal of Structural Integrity and Maintenance (2025) Vol. 10, Iss. 2
Closed Access
Cyber-Physical Systems Security in Space
Mohammad Norman Gaza Laksono, Zhafira Anindya Tiaraputri, Binastya Anggara Sekti, et al.
IGI Global eBooks (2025), pp. 85-114
Closed Access
Advanced machine learning techniques for predicting compressive strength of ultra-high performance concrete
Arslan Qayyum Khan, Syed Ghulam Muhammad, Ali Raza, et al.
Frontiers of Structural and Civil Engineering (2025)
Closed Access
Concrete compressive strength classification using hybrid machine learning models and interactive GUI
Mostafa M. Alsaadawi, Mohamed Kamel Elshaarawy, Abdelrahman Kamal Hamed
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 5
Open Access
Hybrid machine learning modelling and feature interpretation of load-carrying capacity of PVC tube-confined concrete columns
Rupesh Kumar Tipu, Vipin Kumar Verma
Asian Journal of Civil Engineering (2025)
Closed Access
Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review
Dayou Luo, Kejin Wang, Dongming Wang, et al.
npj Materials Sustainability (2025) Vol. 3, Iss. 1
Open Access
Predicting the Rheological Properties of Super-Plasticized Concrete Using Modeling Techniques
Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, et al.
Materials (2022) Vol. 15, Iss. 15, pp. 5208-5208
Open Access | Times Cited: 14
Support vector regression and ANN approach for predicting the ground water quality
Maha Abdallah Alnuwaiser, Muhammad Faisal Javed, M. Ijaz Khan, et al.
Journal of the Indian Chemical Society (2022) Vol. 99, Iss. 7, pp. 100538-100538
Closed Access | Times Cited: 13
Data-driven strategy for evaluating the response of eco-friendly concrete at elevated temperatures for fire resistance construction
Fahad Alsharari, Bawar Iftikhar, Md. Alhaz Uddin, et al.
Results in Engineering (2023) Vol. 20, pp. 101595-101595
Open Access | Times Cited: 7
A Review on Environmental Parameters Monitoring Systems for Power Generation Estimation from Renewable Energy Systems
Samakshi Verma, Y. Lalitha Kameswari, Sonu Kumar
BioNanoScience (2024) Vol. 14, Iss. 4, pp. 3864-3888
Closed Access | Times Cited: 2
Machine learning models to predict mechanical performance properties of modified bituminous mixes: a comprehensive review
Samrity Jalota, Manju Suthar
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 7, pp. 5581-5598
Closed Access | Times Cited: 2
A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models
Yusuf Tahir ALTUNCI
Buildings (2024) Vol. 14, Iss. 12, pp. 3851-3851
Open Access | Times Cited: 2
Predictive modelling of flexural behaviour of polymer composites: a machine learning approach through material extrusion
Akash Jain, Saloni Upadhyay, Kanishka Pathik, et al.
Progress in Additive Manufacturing (2024)
Closed Access | Times Cited: 2
Towards a circular economy: Recapturing battery, metal, and plastic from soil-size and gravel-size municipal solid waste incineration bottom ash using convolutional neural networks
Chun-Yuan Chen, Teng-To Yu
Journal of Cleaner Production (2023) Vol. 432, pp. 139737-139737
Closed Access | Times Cited: 4
Machine learning prediction and optimization of compressive strength for blended concrete by applying ANN and genetic algorithm
G. Satyanarayana, C. Vivek Kumar, R. M. Karthikeyan, et al.
Cogent Engineering (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm
Liuyan Wang, Jiuyong Li, Dong Dai, et al.
(2024)
Open Access | Times Cited: 1
Experimental Verification for Machine-Learning Approaches in Compressive Strength Prediction of Alkali-Activated Concrete
Alaa M. Morsy, Soha Saleh, Ali H. Shalan
Journal of structural design and construction practice. (2024) Vol. 30, Iss. 1
Closed Access | Times Cited: 1
Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete
Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, et al.
Journal of Environmental Management (2024) Vol. 373, pp. 123462-123462
Closed Access | Times Cited: 1
Evaluating 28-Days Performance of Rice Husk Ash Green Concrete under Compression Gleaned from Neural Networks
Sharanjit Singh, Harish Chandra Arora, Aman Kumar, et al.
Advances in Materials Science and Engineering (2023) Vol. 2023, pp. 1-18
Open Access | Times Cited: 3
Concrete Compressive Strength Prediction by Ensemble Machine Learning Approach
Jyoti Thapa
Journal of Engineering and Sciences (2024) Vol. 3, Iss. 1, pp. 66-73
Open Access
Predictive Modeling of UHPC Compressive Strength: Integration of Support Vector Regression and Arithmetic Optimization Algorithm
Liuyan Wang, Jiuyong Li, Dong Dai, et al.
Applied Sciences (2024) Vol. 14, Iss. 17, pp. 8083-8083
Open Access

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