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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:

Machine Learning-Based Method for Predicting Compressive Strength of Concrete
Daihong Li, Zhili Tang, Qian Kang, et al.
Processes (2023) Vol. 11, Iss. 2, pp. 390-390
Open Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

Compressive strength of concrete material using machine learning techniques
Satish Paudel, Anil Pudasaini, Rajesh Kumar Shrestha, et al.
Cleaner Engineering and Technology (2023) Vol. 15, pp. 100661-100661
Open Access | Times Cited: 49
Machine learning and interactive GUI for concrete compressive strength prediction
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 31
Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials
Abdelrahman Kamal Hamed, Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi
Computers & Structures (2025) Vol. 308, pp. 107644-107644
Closed Access | Times Cited: 2
Ensemble Machine-Learning Models for Accurate Prediction of Solar Irradiation in Bangladesh
Md Shafiul Alam, Fahad Saleh Al–Ismail, M. S. Hossain, et al.
Processes (2023) Vol. 11, Iss. 3, pp. 908-908
Open Access | Times Cited: 35
Predicting compressive strength of quarry waste-based geopolymer mortar using machine learning algorithms incorporating mix design and ultrasonic pulse velocity
Navaratnarajah Sathiparan, Pratheeba Jeyananthan
Nondestructive Testing And Evaluation (2024) Vol. 39, Iss. 8, pp. 2486-2509
Closed Access | Times Cited: 10
Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms
Priya Bijalwan, Ashulekha Gupta, Anubhav Mendiratta, et al.
Economies (2024) Vol. 12, Iss. 1, pp. 16-16
Open Access | Times Cited: 8
Benchmarking AutoML solutions for concrete strength prediction: Reliability, uncertainty, and dilemma
Mohammad Amin Hariri‐Ardebili, Parsa Mahdavi, Farhad Pourkamali‐Anaraki
Construction and Building Materials (2024) Vol. 423, pp. 135782-135782
Closed Access | Times Cited: 7
Waste-to-energy poly-generation scheme for hydrogen/freshwater/power/oxygen/heating capacity production; optimized by regression machine learning algorithms
Qiuli Li, Yuchi Leng, Azher M. Abed, et al.
Process Safety and Environmental Protection (2024) Vol. 187, pp. 876-891
Closed Access | Times Cited: 7
Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach
Turki S. Alahmari, Jawad Ashraf, Md. Habibur Rahman Sobuz, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 11
Closed Access | Times Cited: 7
Predicting compressive strength of cement-stabilized earth blocks using machine learning models incorporating cement content, ultrasonic pulse velocity, and electrical resistivity
Navaratnarajah Sathiparan, Pratheeba Jeyananthan
Nondestructive Testing And Evaluation (2023) Vol. 39, Iss. 5, pp. 1045-1069
Closed Access | Times Cited: 20
Deep learning based concrete compressive strength prediction model with hybrid meta-heuristic approach
Deepa A. Joshi, Radhika Menon, R.K. Jain, et al.
Expert Systems with Applications (2023) Vol. 233, pp. 120925-120925
Closed Access | Times Cited: 19
Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength
Siddhi Pandey, Satish Paudel, Kabin Devkota, et al.
International Journal of Construction Management (2024), pp. 1-15
Closed Access | Times Cited: 5
Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete
John F. Vargas, Ana Isabel Oviedo Carrascal, Nathalia Ortega, et al.
Applied Sciences (2024) Vol. 14, Iss. 11, pp. 4426-4426
Open Access | Times Cited: 5
Predicting compressive strength of concrete using advanced machine learning techniques: a combined dataset approach
Abinash Mandal
Asian Journal of Civil Engineering (2025)
Open Access
BGG-REPT and ROF-REPT: ensemble machine learning models for the prediction of compressive strength of concrete
Binh Thai Pham
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
Closed Access
Analysis of the Applicability of Machine Learning Methods in Solving the Problem of Predicting the Implementation of Cluster Batching Factors
S. P., K.D. Sinko
Èlektronnoe modelirovanie (2025) Vol. 47, Iss. 1, pp. 22-39
Closed Access
Estimation of Mechanical Properties of Acrylonitrile-Butadiene-Styrene for Additive Manufacturing Using Artificial Neural Network and Lm-Bp Optimisation Method
G. A. Munshi, Venkatesh M. Kulkarni, Shanoorbaba Yargatti
(2025)
Closed Access
Machine Learning Approaches for Predicting Mechanical Properties of Steel-Fiber-Reinforced Concrete
Huan Huang, Long Lei, Guoxin Xu, et al.
Materials Today Communications (2025), pp. 112149-112149
Closed Access
Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems
Hasan Alkahtani, Theyazn H. H. Aldhyani, Saleh Nagi Alsubari
Sustainability (2023) Vol. 15, Iss. 8, pp. 6973-6973
Open Access | Times Cited: 15
Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings
Ifeyinwa Ijeoma Obianyo, Jonathan Timothy Auta, David Sciacca, et al.
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 4
Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms
Ana Isabel Oviedo Carrascal, Jorge Londoño, John F. Vargas, et al.
Modelling—International Open Access Journal of Modelling in Engineering Science (2024) Vol. 5, Iss. 3, pp. 642-658
Open Access | Times Cited: 3
Parametric study on global estimation models for compressive strength adopting various machine learning algorithms in concrete
Woldeamanuel Minwuye Mesfin, Hyeong-Ki Kim
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 108888-108888
Closed Access | Times Cited: 2
Concrete Compressive Strength Prediction Using Combined Non-Destructive Methods: A Calibration Procedure Using Preexisting Conversion Models Based on Gaussian Process Regression
Giovanni Angiulli, Salvatore Calcagno, Fabio La Foresta, et al.
Journal of Composites Science (2024) Vol. 8, Iss. 8, pp. 300-300
Open Access | Times Cited: 2
An evolutionary functional link artificial neural network for assessment of compressive strength of concrete structures
Sarat Chandra Nayak, Satchidananda Dehuri, Sung‐Bae Cho
Ain Shams Engineering Journal (2023) Vol. 15, Iss. 3, pp. 102462-102462
Open Access | Times Cited: 4
Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction
Xuwei Dong, Yang Liu, Jinpeng Dai
Advances in Civil Engineering (2024) Vol. 2024, pp. 1-15
Open Access | Times Cited: 1
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