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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:

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

Showing 19 citing articles:

Synergistic effects of supplementary cementitious materials and compressive strength prediction of concrete using machine learning algorithms with SHAP and PDP analyses
Rezaul Karim, Md. Hamidul Islam, Shuvo Dip Datta, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02828-e02828
Open Access | Times Cited: 55
Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 45
Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations
Pobithra Das, Abul Kashem
Case Studies in Construction Materials (2023) Vol. 20, pp. e02723-e02723
Open Access | Times Cited: 44
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 31
Estimation and Prediction of the Polymers’ Physical Characteristics Using the Machine Learning Models
Ivan Malashin, В С Тынченко, Vladimir A. Nelyub, et al.
Polymers (2023) Vol. 16, Iss. 1, pp. 115-115
Open Access | Times Cited: 29
Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model
Tianlong Wang, Keying Zhang, Zhenghua Liu, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131347-131347
Closed Access | Times Cited: 10
A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis
Pobithra Das, Abul Kashem, Jasim Uddin Rahat, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3457-3482
Closed Access | Times Cited: 6
Integrating PCA and XGBoost for Predicting UACLC of Steel-Reinforced Concrete-Filled Square Steel Tubular Columns at Elevated Temperatures
Megha Gupta, Satya Prakash, Sufyan Ghani, et al.
Case Studies in Construction Materials (2025), pp. e04456-e04456
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
Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface
Turki S. Alahmari, Kiran Arif
Materials Today Communications (2024) Vol. 40, pp. 109946-109946
Closed Access | Times Cited: 4
Multi-layer retrieval of aerosol optical depth in the troposphere using SEVIRI data: a case study of the European continent
Maryam Pashayi, Mehran Satari, Mehdi Momeni Shahraki
Atmospheric measurement techniques (2025) Vol. 18, Iss. 6, pp. 1415-1439
Open Access
Visual analytics of an interpretable prediction model for the glass transition temperature of fluoroelastomers
Jiling Liu, Yadong Wu, Zhoujun Lin, et al.
Materials Today Communications (2024) Vol. 40, pp. 110155-110155
Open Access | Times Cited: 2
Machine learning models to predict sewer concrete strength exposed to sulfide environments: unveiling the superiority of Bayesian-optimized prediction models
Bilal Siddiq, Muhammad Faisal Javed, Majid Ali Khan, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 6045-6071
Closed Access | Times Cited: 1
Compressive strength of bentonite concrete using state-of-the-art optimised XGBoost models
Prince Kumar, Shivani Kamal, Abhishek Kumar, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-24
Closed Access | Times Cited: 1
Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework
Tasneem A. Awaad, M. Watheq El‐Kharashi, Mohamed Taher, et al.
Sensors (2023) Vol. 23, Iss. 18, pp. 7941-7941
Open Access | Times Cited: 2
Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning
Md Muzammal Hoque, Ajad Shrestha, Sanjog Chhetri Sapkota, et al.
Asian Journal of Civil Engineering (2024)
Closed Access
Machine Learning to Predict Workability and Compressive Strength of Low- and High-Calcium Fly Ash–Based Geopolymers
Andrie Harmaji, Mira Chandra Kirana, Reza Jafari
Crystals (2024) Vol. 14, Iss. 10, pp. 830-830
Open Access
Predictive Analytics in Credit Scoring: Integrating XG Boost and Neural Networks for Enhanced Financial Decision Making
Shailesh Krishna, Mohd Aarif, Narinder Kumar Bhasin, et al.
(2024), pp. 1-6
Closed 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
Stress Distribution Prediction of Circular Hollow Section Tube in Flexible High-Neck Flange Joints Based on the Hybrid Machine Learning Model
Kaoshan Dai, Hang Du, Yuxiao Luo, et al.
Materials (2023) Vol. 16, Iss. 20, pp. 6815-6815
Open Access | Times Cited: 1
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