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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:

Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete
Liborio Cavaleri, Mohammad Sadegh Barkhordari, Constantinos C. Repapis, et al.
Construction and Building Materials (2022) Vol. 359, pp. 129504-129504
Closed Access | Times Cited: 108

Showing 1-25 of 108 citing articles:

Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches
Reyam Ali, Maryam Muayad, Ahmed Salih Mohammed, et al.
Structural Concrete (2022) Vol. 24, Iss. 3, pp. 4161-4184
Closed Access | Times Cited: 92
Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading
Haytham F. Isleem, Naga Dheeraj Kumar Reddy Chukka, Alireza Bahrami, et al.
Results in Engineering (2023) Vol. 19, pp. 101341-101341
Open Access | Times Cited: 49
Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm
Jun Zhang, Ranran Wang, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 3, pp. 591-591
Open Access | Times Cited: 26
Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading
Haytham F. Isleem, Naga Dheeraj Kumar Reddy Chukka, Alireza Bahrami, et al.
Alexandria Engineering Journal (2024) Vol. 92, pp. 380-416
Open Access | Times Cited: 23
Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete
Ranran Wang, Jun Zhang, Yijun Lü, et al.
Buildings (2024) Vol. 14, Iss. 2, pp. 396-396
Open Access | Times Cited: 19
Revealing the nature of soil liquefaction using machine learning
Sufyan Ghani, Ishwor Thapa, Amrendra Kumar, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Open Access | Times Cited: 3
Dimensionless Machine Learning: Dimensional Analysis to Improve LSSVM and ANN models and predict bearing capacity of circular foundations
Hongchao Li, Shahab Hosseini, Behrouz Gordan, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 4
Open Access | Times Cited: 2
Optimizing high-strength concrete compressive strength with explainable machine learning
Sanjog Chhetri Sapkota, Christina Panagiotakopoulou, Dipak Dahal, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 3
Open Access | Times Cited: 2
Prediction of hysteresis response of steel braces using long Short-Term memory artificial neural networks
Sepehr Pessiyan, Fardad Mokhtari, Ali Imanpour
Computers & Structures (2025) Vol. 309, pp. 107672-107672
Open Access | Times Cited: 2
Application of Non-destructive Methods for Predicting the Concrete’s Compressive Strength, Using Hyper-Tuned Machine Learning Techniques
Swati Achra, Ravindra Nagar, Rajesh Kumar, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2025)
Closed Access | Times Cited: 2
Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method
Ali Reza Ghanizadeh, Afshin Ghanizadeh, Panagiotis G. Asteris, et al.
Transportation Geotechnics (2022) Vol. 38, pp. 100906-100906
Closed Access | Times Cited: 65
Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
Mojtaba Yari, Danial Jahed Armaghani, Chrysanthos Maraveas, et al.
Applied Sciences (2023) Vol. 13, Iss. 3, pp. 1345-1345
Open Access | Times Cited: 31
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 31
Decision tree models for the estimation of geo-polymer concrete compressive strength
Ji Zhou, Zhanlin Su, Shahab Hosseini, et al.
Mathematical Biosciences & Engineering (2023) Vol. 21, Iss. 1, pp. 1413-1444
Open Access | Times Cited: 28
Unboxing machine learning models for concrete strength prediction using XAI
Sara Elhishi, Asmaa Mohammed Elashry, Sara El‐Metwally
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 26
Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning
Rakesh Kumar, Pijush Samui, Baboo Rai
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024) Vol. 48, Iss. 5, pp. 3717-3734
Closed Access | Times Cited: 16
Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches
Jitendra Khatti, Berivan Yılmazer Polat
Structures (2024) Vol. 68, pp. 107159-107159
Closed Access | Times Cited: 15
Machine learning and soft voting ensemble classification for earthquake induced damage to bridges
Dipendra Gautam, Ankit Bhattarai, Rajesh Rupakhety
Engineering Structures (2024) Vol. 303, pp. 117534-117534
Closed Access | Times Cited: 10
Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing
Harish Chandra Arora, Bharat Bhushan, Aman Kumar, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
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 | Times Cited: 1
Performance characterisation of machine learning models for geotechnical axial pile load capacity estimation: an enhanced GPR-based approach
Ibrahim Haruna Umar, Mahir Sukairaj Salga, Hang Lin, et al.
Geomechanics and Geoengineering (2025), pp. 1-42
Closed Access | Times Cited: 1
Prediction of Zinc, Cadmium, and Arsenic in European Soils Using Multi-End Machine Learning Models
Mohammad Sadegh Barkhordari, Chongchong Qi
Journal of Hazardous Materials (2025) Vol. 490, pp. 137800-137800
Closed Access | Times Cited: 1
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend
Ramin Kazemi
Engineering Reports (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 21
Forecasting the compressive strength of GGBFS-based geopolymer concrete via ensemble predictive models
Ceren Kına, Harun Tanyıldızı, Kâzım Türk
Construction and Building Materials (2023) Vol. 405, pp. 133299-133299
Closed Access | Times Cited: 19
Corrosion effect on bond behavior between rebar and concrete using Bayesian regularized feed-forward neural network
Thanh-Hung Nguyen, Tan Nguyen, Tam T. Truong, et al.
Structures (2023) Vol. 51, pp. 1525-1538
Closed Access | Times Cited: 18
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