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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 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: 104

Showing 1-25 of 104 citing articles:

Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume
Rakesh Kumar, Shashikant Kumar, Baboo Rai, et al.
Structures (2024) Vol. 66, pp. 106850-106850
Closed Access | Times Cited: 19
Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review
Rakesh Kumar, Baboo Rai, Pijush Samui
Structural Concrete (2024)
Closed Access | Times Cited: 18
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: 8
Exploring LightGBM-SHAP: Interpretable predictive modeling for concrete strength under high temperature conditions
Shaoqiang Meng, Zhenming Shi, Chengzhi Xia, et al.
Structures (2025) Vol. 71, pp. 108134-108134
Closed Access | Times Cited: 4
Triple-stage crack detection in stone masonry using YOLO-ensemble, MobileNetV2U-net, and spectral clustering
Ali Mayya, Nizar Faisal Alkayem
Automation in Construction (2025) Vol. 172, pp. 106045-106045
Closed 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
Development of a prediction tool for the compressive strength of ternary blended ultra-high performance concrete using machine learning techniques
Rakesh Kumar, Shubhum Prakash, Baboo Rai, et al.
Journal of Structural Integrity and Maintenance (2024) Vol. 9, Iss. 3
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
Hybrid random forest models optimized by Sparrow search algorithm (SSA) and Harris hawk optimization algorithm (HHO) for slope stability prediction
Meng Wang, Guoyan Zhao, Shaofeng Wang
Transportation Geotechnics (2024) Vol. 48, pp. 101305-101305
Closed Access | Times Cited: 14
Performance evaluation of hybrid fiber reinforced concrete on engineering properties and life cycle assessment: A sustainable approach
Manish S. Dharek, M. Manjunatha, S Brijbhushan, et al.
Journal of Cleaner Production (2024) Vol. 458, pp. 142498-142498
Closed Access | Times Cited: 13
Prediction of concrete compressive strength using support vector machine regression and non-destructive testing
Wanmao Zhang, Dunwen Liu, Kunpeng Cao
Case Studies in Construction Materials (2024) Vol. 21, pp. e03416-e03416
Open Access | Times Cited: 13
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: 11
Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models
Jitendra Khatti, Kamaldeep Singh Grover
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 6, pp. 4098-4141
Closed Access | Times Cited: 11
Enhancing unconfined compressive strength prediction in nano-silica stabilized soil: a comparative analysis of ensemble and deep learning models
Ishwor Thapa, Sufyan Ghani
Modeling Earth Systems and Environment (2024) Vol. 10, Iss. 4, pp. 5079-5102
Closed Access | Times Cited: 10
Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials
Eka Oktavia Kurniati, Hang Zeng, Marat I. Latypov, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03373-e03373
Open Access | Times Cited: 10
Efficient hybrid ensembles of CNNs and transfer learning models for bridge deck image-based crack detection
Ali Mayya, Nizar Faisal Alkayem, Lei Shen, et al.
Structures (2024) Vol. 64, pp. 106538-106538
Closed Access | Times Cited: 9
Enhancing BOD5 Forecasting Accuracy with the ANN-Enhanced Runge Kutta Model
Rana Muhammad Adnan, Ahmed A. Ewees, Mo Wang, et al.
Journal of environmental chemical engineering (2025), pp. 115430-115430
Closed Access | Times Cited: 1
A machine learning approach to predicting pervious concrete properties: a review
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
Closed Access | Times Cited: 1
Comparative use of different AI methods for the prediction of concrete compressive strength
Mouhamadou Amar
Cleaner Materials (2025) Vol. 15, pp. 100299-100299
Open Access | Times Cited: 1
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
Optimized machine learning models for predicting the tensile strength of high-performance concrete
Divesh Ranjan Kumar, Pramod Kumar, Pradeep Thangavel, et al.
Journal of Structural Integrity and Maintenance (2025) Vol. 10, Iss. 1
Closed Access | Times Cited: 1
A Review of Structural Topology Optimization for Fiber-Reinforced Composites
Xuyu Zhang, Guangyong Sun, Cong Wang, et al.
Composites Part B Engineering (2025), pp. 112393-112393
Open Access | Times Cited: 1
Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm
Somdutta, Baboo Rai
Nondestructive Testing And Evaluation (2025), pp. 1-33
Closed Access | Times Cited: 1
Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools
Han Sun, Muhammad Nasir Amin, Muhammad Tahir Qadir, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03083-e03083
Open Access | Times Cited: 8
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