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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:

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

Showing 19 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: 19
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: 11
Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Materials Today Communications (2024) Vol. 39, pp. 109222-109222
Closed Access | Times Cited: 10
Soft computing models for prediction of bentonite plastic concrete strength
Waleed Bin Inqiad, Muhammad Faisal Javed, Kennedy C. Onyelowe, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Utilizing contemporary machine learning techniques for determining soilcrete properties
Waleed Bin Inqiad, Muhammad Saud Khan, Zeeshan Mehmood, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 1
Prediction and validation of mechanical properties of self-compacting geopolymer concrete using combined machine learning methods a comparative and suitability assessment of the best analysis
Kennedy C. Onyelowe, Ahmed M. Ebid, Paul O. Awoyera, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
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
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
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: 5
Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning
Muhammad Saud Khan, Liqiang Ma, Waleed Bin Inqiad, et al.
Case Studies in Construction Materials (2024) Vol. 22, pp. e04112-e04112
Closed Access | Times Cited: 5
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
Machine learning predictions of high-strength RCA concrete utilizing chemically activated fly ash and nano-silica
Muhammad Adil Khan, Muhammad Nadeem Ashraf, Kennedy C. Onyelowe, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Explainable machine learning model for predicting compressive strength of CO2-cured concrete
Jia Chu, Bingbing Guo, Taotao Zhong, et al.
Case Studies in Construction Materials (2025), pp. e04589-e04589
Open Access
Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete
Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Machine learning prediction of drying shrinkage for alkali-activated materials and multi-objective optimization
Lei Zhang, Dehui Zhu, Moncef L. Nehdi, et al.
Materials Today Communications (2025), pp. 112326-112326
Closed Access
Training strategy and intelligent model for in-situ rapid measurement of subgrade compactness
Xuefei Wang, Xiangdong Li, Jiale Li, et al.
Automation in Construction (2024) Vol. 165, pp. 105581-105581
Closed Access | Times Cited: 2
Interpretability Analysis of Shear Capacity in Reinforced Recycled Aggregate Concrete Beams Using Tree Models
Li Li, Yapeng Qin, Yang Zhang, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 8, pp. 3430-3443
Closed Access
Prediction of Rock Fracture Pressure in Hydraulic Fracturing with Interpretable Machine Learning and Mechanical Specific Energy Theory
Xiaoying Zhuang, Yuhang Liu, Yuwen Hu, et al.
Rock Mechanics Bulletin (2024), pp. 100173-100173
Open Access
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