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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:

Efficient compressive strength prediction of concrete incorporating recycled coarse aggregate using Newton’s boosted backpropagation neural network (NB-BPNN)
Rupesh Kumar Tipu, Vandna Batra, Suman Suman, et al.
Structures (2023) Vol. 58, pp. 105559-105559
Closed Access | Times Cited: 22

Showing 22 citing articles:

ADVANCED MATERIALS FOR SUSTAINABLE CONSTRUCTION: A REVIEW OF INNOVATIONS AND ENVIRONMENTAL BENEFITS
Zamathula Queen Sikhakhane Nwokediegwu, Valentine Ikenna Ilojianya, Kenneth Ifeanyi Ibekwe, et al.
Engineering Science & Technology Journal (2024) Vol. 5, Iss. 1, pp. 201-218
Open Access | Times Cited: 22
Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation
Sanjog Chhetri Sapkota, Ajay Yadav, Ajaya Khatri, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 6073-6096
Closed Access | Times Cited: 6
Tree-based machine learning models for predicting the bond strength in reinforced recycled aggregate concrete
Alireza Mahmoudian, Maryam Bypour, Denise‐Penelope N. Kontoni
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 5
Prediction of Compressive Strength of Fly Ash-Recycled Mortar Based on Grey Wolf Optimizer–Backpropagation Neural Network
Shao Jing-jing, Lin-Bin Li, Guang-Ji Yin, et al.
Materials (2025) Vol. 18, Iss. 1, pp. 139-139
Open Access
Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis
Md Mahamodul Islam, Pobithra Das, Md Mahbubur Rahman, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
Comparing the performance of machine learning models for predicting the compressive strength of concrete
Arthur Afonso Bitencourt Loureiro, Ricardo Stefani
Deleted Journal (2024) Vol. 1, Iss. 1
Open Access | Times Cited: 4
Using meta-heuristic optimization in ANFIS models to estimate compressive strength for recycled aggregate concrete
Xuedi Hong, Jing Wang
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3355-3374
Closed Access | Times Cited: 3
Adaptive Control Strategies for Enhanced Integration of Solar Power in Smart Grids Using Reinforcement Learning
Deepak Singh, Owais Ahmad Shah, Sujata Arora
Energy Storage and Saving (2024)
Open Access | Times Cited: 3
Machine learning-based prediction of concrete strengths with coconut shell as partial coarse aggregate replacement: a comprehensive analysis and sensitivity study
Rupesh Kumar Tipu, V. R. Panchal, Kartik S. Pandya
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 4, pp. 3183-3200
Closed Access | Times Cited: 2
Enhancing Concrete Properties Through the Integration of Recycled Coarse Aggregate: A Machine Learning Approach for Sustainable Construction
Rupesh Kumar Tipu, Owais Ahmad Shah, Satvik Vats, et al.
(2024), pp. 1-5
Closed Access | Times Cited: 2
Advanced Predictive Modeling of Concrete Compressive Strength and Slump Characteristics: A Comparative Evaluation of BPNN, SVM, and RF Models Optimized via PSO
Xuefei Chen, Yi Zhang, Weizhi Chen
Materials (2024) Vol. 17, Iss. 19, pp. 4791-4791
Open Access | Times Cited: 2
Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models
Asad Ullah Khan, Raheel Asghar, Najmul Hassan, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 2
A New Era of Automation in the Construction Industry: Implementing Leading-Edge Generative Artificial Intelligence, such as ChatGPT or Bard
Nitin Rane, Saurabh Choudhary, Jayesh Rane
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 1
A theoretical approach based on machine learning for estimation of physical properties of LLDPE in moulding process
Fan Zhong
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Machine learning-based model for prediction of concrete strength
Vivek Aswal, B. K. Singh, Rohit Maheshwari
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 1
A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models
Yusuf Tahir ALTUNCI
Buildings (2024) Vol. 14, Iss. 12, pp. 3851-3851
Open Access | Times Cited: 1
Comparative strength estimation model of recycled aggregate concrete modified with GGBS, Metakaolin, and fly ash
Lina Zhang, Yuqing Tian, Shan Deng
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5461-5479
Closed Access
Enhancing the predictive accuracy of recycled aggregate concrete’s strength using machine learning and statistical approaches: a review
Jawad Tariq, Kui Hu, Syed Tafheem Abbas Gillani, et al.
Asian Journal of Civil Engineering (2024)
Closed Access
An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product
Ramin Kazemi, Seyedali Mirjalili
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Prediction of fresh and hardened concrete properties using machine learning algorithms
Pranjal V. Chechani, Shashi Bhushan Kumar, Subhro Chakraborty, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 12
Closed Access
Metaheuristic-based machine learning approaches of compressive strength forecasting of steel fiber reinforced concrete with SHapley Additive exPlanations
Abul Kashem, Ayesha Anzer, Ravi Jagirdar, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access
A comparative study of LSSVR analysis on ground granulated blast-furnace slag-based concrete
Pu Zhou, Yin Lunyu
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access
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