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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 compressive strength modelling of concrete incorporating waste marble powder
Elyas Asadi Shamsabadi, Naeim Roshan, S.A. Hadigheh, et al.
Construction and Building Materials (2022) Vol. 324, pp. 126592-126592
Closed Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Optimization design for alkali-activated slag-fly ash geopolymer concrete based on artificial intelligence considering compressive strength, cost, and carbon emission
Yue Li, Jiale Shen, Hui Lin, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 106929-106929
Closed Access | Times Cited: 66
RETRACTED: Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation
Hemn Unis Ahmed, Ahmed Salih Mohammed, Azad A. Mohammed
Construction and Building Materials (2023) Vol. 394, pp. 132266-132266
Closed Access | Times Cited: 51
Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete
Seyed Soroush Pakzad, Naeim Roshan, Mansour Ghalehnovi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 50
Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete
Celal Çakıroğlu, Md. Shahjalal, Kamrul Islam, et al.
Journal of Building Engineering (2023) Vol. 76, pp. 107279-107279
Closed Access | Times Cited: 49
Splitting tensile strength of basalt fiber reinforced coral aggregate concrete: Optimized XGBoost models and experimental validation
Zhen Sun, Yalin Li, Yuxi Yang, et al.
Construction and Building Materials (2024) Vol. 416, pp. 135133-135133
Closed Access | Times Cited: 49
Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0
Xing Quan Wang, Pengguang Chen, Cheuk Lun Chow, et al.
Matter (2023) Vol. 6, Iss. 6, pp. 1831-1859
Open Access | Times Cited: 45
The use of machine learning techniques to investigate the properties of metakaolin-based geopolymer concrete
Seyed Ali Eftekhar Afzali, Mohsen Ali Shayanfar, Mohammad Ghanooni-Bagha, et al.
Journal of Cleaner Production (2024) Vol. 446, pp. 141305-141305
Open Access | Times Cited: 23
Prediction of compressive strength and tensile strain of engineered cementitious composite using machine learning
Md Nasir Uddin, N. Shanmugasundaram, S. Praveenkumar, et al.
International Journal of Mechanics and Materials in Design (2024) Vol. 20, Iss. 4, pp. 671-716
Closed Access | Times Cited: 13
Development of machine learning models for the prediction of the compressive strength of calcium-based geopolymers
Wangwen Huo, Zhiduo Zhu, He Sun, et al.
Journal of Cleaner Production (2022) Vol. 380, pp. 135159-135159
Closed Access | Times Cited: 65
Compressive strength prediction of one-part alkali activated material enabled by interpretable machine learning
Syed Farasat Ali Shah, Bing Chen, Muhammad Zahid, et al.
Construction and Building Materials (2022) Vol. 360, pp. 129534-129534
Closed Access | Times Cited: 49
Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process
Ahmad Hosseinzadeh, John L. Zhou, Javad Zyaie, et al.
Separation and Purification Technology (2022) Vol. 289, pp. 120775-120775
Closed Access | Times Cited: 37
Experimental study and machine learning prediction on compressive strength of spontaneous-combustion coal gangue aggregate concrete
Tirui Zhang, Yuzhuo Zhang, Qinghe Wang, et al.
Journal of Building Engineering (2023) Vol. 71, pp. 106518-106518
Closed Access | Times Cited: 32
Modified particle packing approach for optimizing waste marble powder as a cement substitute in high-performance concrete
Ahmed Essam, Sahar A. Mostafa, Mehran Khan, et al.
Construction and Building Materials (2023) Vol. 409, pp. 133845-133845
Closed Access | Times Cited: 30
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Hai‐Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 27
Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars
Ning Wang, Majid Samavatian, Vahid Samavatian, et al.
Materials Today Communications (2023) Vol. 35, pp. 106283-106283
Closed Access | Times Cited: 25
Prediction of durability of reinforced concrete based on hybrid-Bp neural network
Qiong Feng, Xiaoyang Xie, Penghui Wang, et al.
Construction and Building Materials (2024) Vol. 425, pp. 136091-136091
Closed Access | Times Cited: 13
A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete
Seyed Soroush Pakzad, Mansour Ghalehnovi, Atiye Ganjifar
Case Studies in Construction Materials (2024) Vol. 20, pp. e03092-e03092
Open Access | Times Cited: 10
Predicting compressive strength of fiber-reinforced coral aggregate concrete: Interpretable optimized XGBoost model and experimental validation
Zhen Sun, Xin Wang, Huang Huang, et al.
Structures (2024) Vol. 64, pp. 106516-106516
Closed Access | Times Cited: 10
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 9
High-Strength Self-Compacting Concrete Production Incorporating Supplementary Cementitious Materials: Experimental Evaluations and Machine Learning Modelling
Md. Habibur Rahman Sobuz, Fahim Shahriyar Aditto, Shuvo Dip Datta, et al.
International Journal of Concrete Structures and Materials (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 8
Evaluation of textile effluent treatment plant sludge as supplementary cementitious material in concrete using experimental and machine learning approaches
Md Mottakin, Shuvo Dip Datta, Md. Mehrab Hossain, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110627-110627
Closed Access | Times Cited: 7
The Strength, Microstructure, and ecological assessment of concrete mix incorporating waste glass powder and polypropylene fiber
Atena Asgarian, Naeim Roshan, Mansour Ghalehnovi
Construction and Building Materials (2023) Vol. 371, pp. 130726-130726
Closed Access | Times Cited: 15
Machine learning-enabled characterization of concrete mechanical strength through correlation of flexural and torsional resonance frequencies
Bai Li, Majid Samavatian, Vahid Samavatian
Physica Scripta (2024) Vol. 99, Iss. 7, pp. 076002-076002
Closed Access | Times Cited: 6
A novel compressive strength estimation approach for 3D printed fiber-reinforced concrete: integrating machine learning and gene expression programming
Md Nasir Uddin, Junhong Ye, M. Aminul Haque, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 5, pp. 4889-4910
Closed Access | Times Cited: 5
Performance evaluation of the strength properties of sustainable concrete utilizing waste marble dust and glass fibre employing artificial neural network and particle swarm optimization algorithm
Prashant Lahre, Kundan Meshram, Umank Mishra, et al.
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
Closed Access
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