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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:

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete
Jesús de‐Prado‐Gil, Covadonga Palencia, P. Jagadesh, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4164-4164
Open Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 65
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: 57
Assessing the influence of sugarcane bagasse ash for the production of eco-friendly concrete: Experimental and machine learning approaches
Md. Habibur Rahman Sobuz, Al-Imran, Shuvo Dip Datta, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02839-e02839
Open Access | Times Cited: 54
Optimum usage of waste marble powder to reduce use of cement toward eco-friendly concrete
Yasin Onuralp Özkılıç, Özer Zeybek, Alireza Bahrami, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 4799-4819
Open Access | Times Cited: 48
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: 19
Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges
Sanduni Jayasinghe, Mojtaba Mahmoodian, Azadeh Alavi, et al.
CivilEng (2025) Vol. 6, Iss. 1, pp. 2-2
Open Access | Times Cited: 2
Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment
Qiuying Chang, Lanlan Liu, Muhammad Usman Farooqi, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 6348-6368
Open Access | Times Cited: 42
Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms
Maedeh Hosseinzadeh, Mehdi Dehestani, Alireza Hosseinzadeh
Journal of Building Engineering (2023) Vol. 76, pp. 107006-107006
Closed Access | Times Cited: 42
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: 12
Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete
Ismail B. Mustapha, Muyideen Abdulkareem, Taha M. Jassam, et al.
International Journal of Concrete Structures and Materials (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 12
Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete
Mohammed Alarfaj, Hisham Jahangir Qureshi, Muhammad Zubair Shahab, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02836-e02836
Open Access | Times Cited: 10
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: 10
Assessment of compressive strength of eco-concrete reinforced using machine learning tools
Houcine Bentegri, Mohamed Rabehi, Samir Kherfane, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications
Duygu Ege, Seda Sertturk, Berk Acarkan, et al.
Biomedical Physics & Engineering Express (2023) Vol. 9, Iss. 6, pp. 065014-065014
Open Access | Times Cited: 20
Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete
Waleed Bin Inqiad, Muhammad Shahid Siddique, Saad S. Alarifi, et al.
Heliyon (2023) Vol. 9, Iss. 11, pp. e22036-e22036
Open Access | Times Cited: 19
Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI)
Waleed Bin Inqiad, Elena Valentina Dumitrascu, Robert Alexandru Dobre, et al.
Heliyon (2024) Vol. 10, Iss. 17, pp. e36841-e36841
Open Access | Times Cited: 8
Predicting natural vibration period of concrete frame structures having masonry infill using machine learning techniques
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110417-110417
Closed Access | Times Cited: 7
Predicting parameters and sensitivity assessment of nano-silica-based fiber-reinforced concrete: a sustainable construction material
Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sufian, et al.
Journal of Materials Research and Technology (2023) Vol. 23, pp. 3943-3960
Open Access | Times Cited: 15
Machine learning algorithms to optimize the properties of bio-based poly(butylene succinate-co- butylene adipate) nanocomposites with carbon nanotubes
Elizabeth Champa-Bujaico, Ana M. Díez‐Pascual, Pilar García-Díaz, et al.
Industrial Crops and Products (2024) Vol. 219, pp. 119018-119018
Open Access | Times Cited: 6
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
Waleed Bin Inqiad, Muhammad Shahid Siddique, Mujahid Ali, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Artificial neural network, machine learning modelling of compressive strength of recycled coarse aggregate based self-compacting concrete
P. Jagadesh, Afzal Husain Khan, B. Shanmuga Priya, et al.
PLoS ONE (2024) Vol. 19, Iss. 5, pp. e0303101-e0303101
Open Access | Times Cited: 5
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
Machine Learning-Based Predictive Modeling of Sustainable Lightweight Aggregate Concrete
Fazal Hussain, Shayan Ali Khan, Rao Arsalan Khushnood, et al.
Sustainability (2022) Vol. 15, Iss. 1, pp. 641-641
Open Access | Times Cited: 20
Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures
Waleed Bin Inqiad, Elena Valentina Dumitrascu, Robert Alexandru Dobre
Heliyon (2024) Vol. 10, Iss. 12, pp. e32856-e32856
Open Access | Times Cited: 5
A Robust LightGBM Model for Concrete Tensile Strength Forecast to Aid in Resilience-based Structure Strategies
Chukwuemeka Daniel
Heliyon (2024) Vol. 10, Iss. 20, pp. e39679-e39679
Open Access | Times Cited: 4
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