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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:

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete
Muhammad Nasir Amin, Mohammed Najeeb Al-Hashem, Ayaz Ahmad, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7800-7800
Open Access | Times Cited: 18

Showing 18 citing articles:

Rice Husk Ash in Concrete
Solomon Asrat Endale, Woubishet Zewdu Taffese, Duy‐Hai Vo, et al.
Sustainability (2022) Vol. 15, Iss. 1, pp. 137-137
Open Access | Times Cited: 64
A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete
Akshita Bassi, Aditya Manchanda, Rajwinder Singh, et al.
Natural Hazards (2023) Vol. 118, Iss. 1, pp. 209-238
Closed Access | Times Cited: 24
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: 14
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
Fatemeh Mobasheri, Masoud Hosseinpoor, Ammar Yahia, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access | Times Cited: 1
Machine learning approaches for real-time prediction of compressive strength in self-compacting concrete
Sufyan Ghani, Nishant Kumar, Megha Gupta, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 3, pp. 2743-2760
Closed Access | Times Cited: 20
Optimizing machine learning techniques and SHapley Additive exPlanations (SHAP) analysis for the compressive property of self-compacting concrete
Zhiyuan Wang, Huihui Liu, Muhammad Nasir Amin, et al.
Materials Today Communications (2024) Vol. 39, pp. 108804-108804
Closed Access | Times Cited: 7
Analyzing the efficacy of waste marble and glass powder for the compressive strength of self-compacting concrete using machine learning strategies
Qing Guan, Zhong Ling Tong, Muhammad Nasir Amin, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 5
Data-driven approaches for predicting and optimizing the compressive strength of self-compacting concrete
Tan‐Duy Phan, Duy‐Liem Nguyen
Materials Today Communications (2024), pp. 111298-111298
Closed Access | Times Cited: 4
Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models
Zhiqiang Chen, Bawar Iftikhar, Ayaz Ahmad, et al.
Materials Today Communications (2023) Vol. 37, pp. 107356-107356
Closed Access | Times Cited: 11
Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting algorithms
Sarmed Wahab, Babatunde Abiodun Salami, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03084-e03084
Open Access | Times Cited: 3
Estimation of Compressive Strength of Rubberised Slag Based Geopolymer Concrete Using Various Machine Learning Techniques Based Models
Sesha Choudary Yeluri, Karan Singh, Akshay Kumar, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024)
Closed Access | Times Cited: 3
Application of green material on durability behaviour of green concrete
Swapneel Satone, Dhanajay K. Parbat, Avinash M. Badar, et al.
Materials Today Proceedings (2023)
Closed Access | Times Cited: 4
Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete
Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, et al.
Journal of Environmental Management (2024) Vol. 373, pp. 123462-123462
Closed Access | Times Cited: 1
Self-Compacting Concrete (SCC) Strength Prediction via Optimized Duple-Deep-Learning Model and Distance Ranked Fire-Hawk Optimizer (DRFO)
Mu'tasime Abdel-jaber, Rob Beale, Nisrine Makhoul
Civil Engineering and Architecture (2023) Vol. 11, Iss. 5, pp. 2447-2460
Open Access | Times Cited: 2
Usage of soft-computing techniques to assess the compressive strength of SCC
Hailong Liu, Yan Wang
Multiscale and Multidisciplinary Modeling Experiments and Design (2023) Vol. 7, Iss. 1, pp. 321-334
Closed Access | Times Cited: 1
Comparative study of statistical computational approaches to investigate the degraded compressive strength of concrete under the freeze-thaw effect
Yuanzhong Yang, Naseer Muhammad Khan, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03744-e03744
Open Access
Self-compacting concrete strength evaluation using fire hawk optimization-based simulations
Ronghua Ma
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access
Prediction of Compaction Parameters Based on the Atterberg Limit by Using a Machine Learning Approach
Semachew Molla Kassa, Betelhem Zewdu Wubineh, Africa M. Geremew, et al.
EAI/Springer Innovations in Communication and Computing (2024), pp. 133-146
Closed Access
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