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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:

Estimation of concrete materials uniaxial compressive strength using soft computing techniques
Matiur Rahman Raju, Mahfuzur Rahman, Md Mehedi Hasan, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e22502-e22502
Open Access | Times Cited: 9

Showing 9 citing articles:

Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 12
Modeling Variability in Seismic Analysis of Concrete Gravity Dams: A Parametric Analysis of Koyna and Pine Flat Dams
Bikram Kesharee Patra, Rocio L. Segura, Ashutosh Bagchi
Infrastructures (2024) Vol. 9, Iss. 1, pp. 10-10
Open Access | Times Cited: 5
Utilizing Construction and Demolition Waste in Concrete as a Sustainable Cement Substitute: A Comprehensive Study on Behavior Under Short-term Dynamic and Static Loads via Laboratory and Numerical Analysis
Mohammad Mohtasham Moein, Komeil Rahmati, Ali Mohtasham Moein, et al.
Journal of Building Engineering (2024), pp. 110778-110778
Closed Access | Times Cited: 5
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete
Tariq Ali, Mohamed Hechmi El Ouni, Muhammad Zeeshan Qureshi, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137370-137370
Closed Access | Times Cited: 4
Modelling the properties of aerated concrete on the basis of raw materials and ash-and-slag wastes using machine learning paradigm
О. В. Руденко, Darya Galkina, Marzhan Anuarbekovna Sadenova, et al.
Frontiers in Materials (2024) Vol. 11
Open Access | Times Cited: 2
AI-infused characteristics prediction and multi-objective design of ultra-high performance concrete (UHPC): From pore structures to macro-performance
Wangyang Xu, Lingyan Zhang, Dingqiang Fan, et al.
Journal of Building Engineering (2024), pp. 111170-111170
Closed Access | Times Cited: 1
Geo-Environmental Risk Assessment of Sand Dunes Encroachment Hazards in Arid Lands Using Machine Learning Techniques
Ahmed K. Abd El Aal, Hossam M. GabAllah, Hanaa A. Megahed, et al.
Sustainability (2024) Vol. 16, Iss. 24, pp. 11139-11139
Open Access | Times Cited: 1
Prediction of the Strength of the Concrete-Filled Tubular Steel Columns Using the Artificial Intelligence
Tatiana Kondratieva, Антон Чепурненко
Modern Trends in Construction Urban and Territorial Planning (2024) Vol. 3, Iss. 3, pp. 40-48
Closed Access
Soft Computing for Comprehensive Concrete Strength Prediction – A Comparative Study
S. R. Mugunthan
Journal of Soft Computing Paradigm (2023) Vol. 5, Iss. 4, pp. 417-432
Open Access
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