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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:

Compressive strength of concrete material using machine learning techniques
Satish Paudel, Anil Pudasaini, Rajesh Kumar Shrestha, et al.
Cleaner Engineering and Technology (2023) Vol. 15, pp. 100661-100661
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Machine learning and interactive GUI for concrete compressive strength prediction
Mohamed Kamel Elshaarawy, Mostafa M. Alsaadawi, Abdelrahman Kamal Hamed
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 31
Evaluating the Sensitivity of Machine Learning Models to Data Preprocessing Technique in Concrete Compressive Strength Estimation
Maan Habib, Maan Okayli
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 10, pp. 13709-13727
Closed Access | Times Cited: 16
Machine learning and nonlinear finite element analysis of fiber‐reinforced polymer‐confined concrete‐steel double‐skin tubular columns under axial compression
Haytham F. Isleem, Qiong Tang, Naga Dheeraj Kumar Reddy Chukka, et al.
Structural Concrete (2024)
Closed Access | Times Cited: 14
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: 2
Sustainable mix design and carbon emission analysis of recycled aggregate concrete based on machine learning and big data methods
Boqun Zhang, Lei Pan, X. C. Chang, et al.
Journal of Cleaner Production (2025) Vol. 489, pp. 144734-144734
Closed Access | Times Cited: 1
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
Enhancing Discharge Prediction over Type-A Piano Key Weirs: An Innovative Machine Learning Approach
Wei‐Ming Tian, Haytham F. Isleem, Abdelrahman Kamal Hamed, et al.
Flow Measurement and Instrumentation (2024) Vol. 100, pp. 102732-102732
Closed Access | Times Cited: 12
A novel data-driven machine learning techniques to predict compressive strength of fly ash and recycled coarse aggregates based self-compacting concrete
Surbhi Gupta Aggarwal, Rajwinder Singh, Ayush Rathore, et al.
Materials Today Communications (2024) Vol. 39, pp. 109294-109294
Closed Access | Times Cited: 9
Enhancing compressive strength prediction in self-compacting concrete using machine learning and deep learning techniques with incorporation of rice husk ash and marble powder
Muhammad Sarmad Mahmood, Ayub Elahi, Osama Zaid, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02557-e02557
Open Access | Times Cited: 17
Machine learning unveils the complex nonlinearity of concrete materials’ uniaxial compressive strength
Siddhi Pandey, Satish Paudel, Kabin Devkota, et al.
International Journal of Construction Management (2024), pp. 1-15
Closed 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
Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes
Shriram Marathe, Anisha P Rodrigues
Studia Geotechnica et Mechanica (2024)
Open Access | Times Cited: 5
Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength
Bashar Alibrahim, Ahed Habib, Maan Habib
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength
Manish Tak, Yanxiao Feng, Mohamed Mahgoub
Infrastructures (2025) Vol. 10, Iss. 2, pp. 26-26
Open Access
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
Predicting the compressive strength of rubberized concrete incorporating brick powder based on MLP and RBF neural networks
David Sinkhonde, Destine Mashava, Tajebe Bezabih, et al.
Waste Management Bulletin (2025)
Open Access
BGG-REPT and ROF-REPT: ensemble machine learning models for the prediction of compressive strength of concrete
Binh Thai Pham
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
Closed Access
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
Predicting Mechanical Properties of Concrete Structures Using Metaheuristic-Optimization-Based Machine Learning Models
Ngoc‐Mai Nguyen
Applied Soft Computing (2025), pp. 112893-112893
Closed Access
Analyzing the Behavior of Geopolymer Concrete with Different Novel Machine-Learning Algorithms
Sanjog Chhetri Sapkota, Dipak Dahal, Ajay Kumar Yadav, et al.
Journal of structural design and construction practice. (2025) Vol. 30, Iss. 3
Closed Access
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
Data-Driven Predictive Modeling of Steel Slag Concrete Strength for Sustainable Construction
Asad S. Albostami, Rwayda Kh. S. Al‐Hamd, Ali Ammar Al-Matwari
Buildings (2024) Vol. 14, Iss. 8, pp. 2476-2476
Open Access | Times Cited: 4
Prediction of ultimate strength for high strength concrete (HSC) using machine learning approaches - optimized by PSO technique
P. Ruba, S. Aarthi, P. Bhuvaneshwari
Deleted Journal (2025) Vol. 28, Iss. 1
Open Access
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: 10
Evaluating and strengthening low-rise reinforced concrete buildings constructed in Nepal
Bikram Bhusal, Anuj Pradhananga, Satish Paudel, et al.
Structures (2024) Vol. 63, pp. 106388-106388
Closed Access | Times Cited: 3
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