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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:

Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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: 58
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, Nashwan Adnan Othman, et al.
Results in Engineering (2025), pp. 103909-103909
Open Access | Times Cited: 3
Sustainable mix design and carbon emission analysis of recycled aggregate concrete based on machine learning and big data methods
Boqun Zhang, Lei Pan, X. C. Chang, et al.
Journal of Cleaner Production (2025) Vol. 489, pp. 144734-144734
Closed Access | Times Cited: 2
Prediction and comparison of burning rate of n-heptane pool fire in open space based on BPNN and XGBoost
Peng Xu, Yubo Bi, Jian Chen, et al.
Process Safety and Environmental Protection (2024) Vol. 189, pp. 89-101
Closed Access | Times Cited: 9
Predicting the compressive strength of engineered geopolymer composites using automated machine learning
Mahmoud Anwar Gad, Ehsan Nikbakht, Mohammed Gamal Ragab
Construction and Building Materials (2024) Vol. 442, pp. 137509-137509
Closed Access | Times Cited: 9
Data driven design of ultra high performance concrete prospects and application
Bryan K. Aylas-Paredes, Taihao Han, Advaith Neithalath, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Enhancing load capacity prediction of column using eReLU-activated BPNN model
Rupesh Kumar Tipu, Vandna Batra, Suman Suman, et al.
Structures (2023) Vol. 58, pp. 105600-105600
Closed Access | Times Cited: 14
Intelligent predicting and monitoring of ultra-high-performance fiber reinforced concrete composites − A review
Dingqiang Fan, Ziao Chen, Yuan Cao, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108555-108555
Closed Access | Times Cited: 6
Explainable Ensemble Learning and Multilayer Perceptron Modeling for Compressive Strength Prediction of Ultra-High-Performance Concrete
Yaren Aydın, Celal Çakıroğlu, Gebrai̇l Bekdaş, et al.
Biomimetics (2024) Vol. 9, Iss. 9, pp. 544-544
Open Access | Times Cited: 5
Predictive and experimental assessment of chloride ion permeation in concrete subjected to multi-factorial conditions using the XGBoost algorithm
Xuanrui Yu, Tianyu Hu, Nima Khodadadi, et al.
Journal of Building Engineering (2024) Vol. 98, pp. 111041-111041
Closed Access | Times Cited: 5
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
An empirical studies on online gender-based violence: Classification analysis utilizing XGBOOST
Arum Handini Primandari, Putri Ermayani
AIP conference proceedings (2025) Vol. 3248, pp. 040003-040003
Closed Access
Energy-saving optimization of air-conditioning water system based on machine learning and improved bat algorithm
Yan Bai, Di Sun, L. Li, et al.
Energy and Buildings (2025), pp. 115333-115333
Closed Access
Design of sustainable mortar incorporating construction and demolition waste through adaptive experiments accelerated by machine learning
Thomas Tawiah Baah, Hang Zeng, Marat I. Latypov, et al.
Results in Engineering (2025), pp. 104264-104264
Open Access
Framework for investigating structure cracking using real engineering data combined with physics constraints
Han Si, Qiang Wang, Xin Ruan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
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
Novel approaches in prediction of tensile strain capacity of engineered cementitious composites using interpretable approaches
Turki S. Alahmari, Furqan Farooq
REVIEWS ON ADVANCED MATERIALS SCIENCE (2025) Vol. 64, Iss. 1
Open Access
Optimizing NGBoost with Dynamic Sequential Model-Based Optimization for Predicting UHPC Compressive Strength on Heterogeneous Datasets
Taimur Rahman, Md. Farhad Momin, S. Podder, et al.
Materials Today Communications (2025), pp. 112405-112405
Closed Access
Reinforcement effects of bonding Fe-SMA in steel bridge diaphragms based on machine learning
Yue Shu, Qiang Xu, Xu Jiang, et al.
Structures (2025) Vol. 76, pp. 108984-108984
Closed Access
Study on the degradation models based on the experiments considering the coupling effect of freeze-thaw and carbonation
Qianting Yang, Ming Liu, Jiaxu Li, et al.
Structures (2024) Vol. 64, pp. 106659-106659
Closed Access | Times Cited: 3
Understanding and predicting micro-characteristics of ultra-high performance concrete (UHPC) with green porous lightweight aggregates: Insights from machine learning techniques
Lingyan Zhang, Wangyang Xu, Dingqiang Fan, et al.
Construction and Building Materials (2024) Vol. 446, pp. 138021-138021
Closed Access | Times Cited: 3
Parametric evaluation and prediction of design parameters of geofoam using artificial neural network and extreme gradient boosting models
Parvathi Geetha Sreekantan, Aali Pant, G. V. Ramana
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 7
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
An improved method for calculating roll deformation of six-high rolling mill: enhances computation speed and accuracy
Yafei Chen, Pingjie Feng, Jihan Zhou, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access | Times Cited: 1
Eco-Friendly Nanotechnology in Rheumatoid Arthritis: ANFIS-XGBoost Enhanced Layered Nanomaterials
Zhiyong Zhang, Mingtao Ye, Yisu Ge, et al.
Environmental Research (2024) Vol. 262, pp. 119832-119832
Closed Access | Times Cited: 1
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