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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:

Metaheuristic‐based machine learning modeling of the compressive strength of concrete containing waste glass
Mohamed El Amine Ben Seghier, Emadaldin Mohammadi Golafshani, Jafar Jafari‐Asl, et al.
Structural Concrete (2023) Vol. 24, Iss. 4, pp. 5417-5440
Open Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete
Mahzad Esmaeili‐Falak, Reza Sarkhani Benemaran
Structural Concrete (2024) Vol. 25, Iss. 1, pp. 716-737
Closed Access | Times Cited: 43
Microstructural behavior and explainable machine learning aided mechanical strength prediction and optimization of recycled glass-based solid waste concrete
Md. Habibur Rahman Sobuz, Md. Kawsarul Islam Kabbo, Turki S. Alahmari, et al.
Case Studies in Construction Materials (2025), pp. e04305-e04305
Open Access | Times Cited: 4
Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis
Asif Ahmed, Wei Song, Yumeng Zhang, et al.
Materials (2023) Vol. 16, Iss. 12, pp. 4366-4366
Open Access | Times Cited: 19
Predicting Compressive Strength of High-Performance Concrete Using Hybridization of Nature-Inspired Metaheuristic and Gradient Boosting Machine
Nhat‐Duc Hoang, Van-Duc Tran, Xuan-Linh Tran
Mathematics (2024) Vol. 12, Iss. 8, pp. 1267-1267
Open Access | Times Cited: 6
Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network
Majid Khan, Muhammad Faisal Javed, N. Othman, et al.
Results in Engineering (2025), pp. 103909-103909
Open Access
An Interpretable XGBoost-SHAP Machine Learning Model for Reliable Prediction of Mechanical Properties in Waste Foundry Sand-Based Eco-Friendly Concrete
Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, et al.
Results in Engineering (2025), pp. 104307-104307
Open Access
A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data
Mohamed El Amine Ben Seghier, Tam T. Truong, Christian Feiler, et al.
Results in Engineering (2025), pp. 104417-104417
Open Access
Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete
Turki S. Alahmari, Md. Kawsarul Islam Kabbo, Md. Habibur Rahman Sobuz, et al.
Materials Today Communications (2025), pp. 112081-112081
Closed Access
A Hybrid Model for Predicting the Energy Dissipation on the Block Ramp Hydraulic Structures
Mostafa Rahmanshahi, Jafar Jafari‐Asl, Mahmood Shafai Bejestan, et al.
Water Resources Management (2023) Vol. 37, Iss. 8, pp. 3187-3209
Closed Access | Times Cited: 13
Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms
Ali Aldrees, Muhammad Faisal Javed, Majid Khan, et al.
Journal of Water Process Engineering (2024) Vol. 66, pp. 105937-105937
Closed Access | Times Cited: 4
An integral approach for testing and computational analysis of glass powder in cementitious composites
Muhammad Nasir Amin, Sohaib Nazar, Mohammed Najeeb Al-Hashem, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02063-e02063
Open Access | Times Cited: 7
Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
M. M. Zaki, Shaojie Chen, Jicheng Zhang, et al.
Applied Sciences (2023) Vol. 13, Iss. 13, pp. 7622-7622
Open Access | Times Cited: 7
Compressive strength prediction of sustainable concrete containing waste foundry sand using metaheuristic optimization‐based hybrid artificial neural network
Ramin Kazemi, Emadaldin Mohammadi Golafshani, Ali Behnood
Structural Concrete (2023) Vol. 25, Iss. 2, pp. 1343-1363
Closed Access | Times Cited: 7
Optimizing compressive strength in sustainable concrete: a machine learning approach with iron waste integration
Rupesh Kumar Tipu, Vandna Batra, Suman Suman, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 6, pp. 4487-4512
Closed Access | Times Cited: 2
A new formulation for predicting the ultimate capacities of FRP-confined concrete using advanced machine learning framework: Developed structural reliability analysis
Mahmoud Alfouneh, Mohamed El Amine Ben Seghier, Behrooz Keshtegar, et al.
Structures (2024) Vol. 68, pp. 107158-107158
Closed Access | Times Cited: 2
Particle swarm optimization tuned multi-headed long short-term memory networks approach for fuel prices forecasting
Andjela Jovanovic, Luka Jovanovic, Miodrag Živković, et al.
Journal of Network and Computer Applications (2024) Vol. 233, pp. 104048-104048
Closed 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
Two-stage framework for lateral-torsional buckling resistance prediction of cellular steel beams under fire conditions
Mohamed El Amine Ben Seghier, Nehal Elshaboury, Eslam Mohammed Abdelkader, et al.
Structures (2024) Vol. 68, pp. 107157-107157
Closed Access | Times Cited: 1
Chloride diffusion modeling of concrete using tree‐based forest models
Emadaldin Mohammadi Golafshani, Alireza Kashani, Mehrdad Arashpour
Structural Concrete (2023) Vol. 24, Iss. 4, pp. 5614-5634
Open Access | Times Cited: 3
Application of machine learning models for the compressive strength prediction of concrete with glass waste powder
Miljan Kovačević, Ivanka Netinger Grubeša, Marijana Hadzima-Nyarko, et al.
Elsevier eBooks (2024), pp. 123-149
Closed Access
Dwarf mongoose-tree-based analysis for estimating the frost durability of recycled aggregate concrete
Lingtong Zhang, Qinglin Zhang, Sheng Liang, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024)
Closed Access
Determination of creep function coefficients of viscoelastic pipes using a transient-guided machine learning model
Ebrahim Sharififard, Mohamad Azizipour, Javad Ahadiyan, et al.
AQUA - Water Infrastructure Ecosystems and Society (2024) Vol. 73, Iss. 11, pp. 2132-2149
Open 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
Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia
Uaktho Baishnab, Md. Sahadat Hossen Sajib, Ashraful Islam, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access
An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions
Xu‐Feng Huang, Junhui Huang, Sakdirat Kaewunruen
Waste Management (2024) Vol. 193, pp. 539-550
Open Access
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