Open Access Helper

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:

Predicting the Compressive Strength of Ultra-High-Performance Concrete Based on Machine Learning Optimized by Meta-Heuristic Algorithm
Yuanyuan Li, Xinxin Yang, Changyun Ren, et al.
Buildings (2024) Vol. 14, Iss. 5, pp. 1209-1209
Open Access | Times Cited: 9

Showing 9 citing articles:

Hybrid machine learning model to predict the mechanical properties of ultra-high-performance concrete (UHPC) with experimental validation
Ajad Shrestha, Sanjog Chhetri Sapkota
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 7, pp. 5227-5244
Closed Access | Times Cited: 5
Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning
Hao Ji, Wenbin Jiao, Xie Xu, et al.
Case Studies in Construction Materials (2025) Vol. 22, pp. e04255-e04255
Open Access
Deep learning enhanced framework for multi-objective optimization of cement-slag concrete for the balancing performance, economics, and sustainability
Amol Shivaji Mali, Atul Kolhe, Pravin Gorde, et al.
Asian Journal of Civil Engineering (2025)
Closed Access
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: 4
Design optimization of high-performance, cost-efficient concrete enhanced with nano-CNTs: A hybrid approach using machine learning and NSGA-II
A. A. Ebrahim
Materials Today Communications (2025), pp. 112251-112251
Closed Access
An Experimental Investigation to Predict the Compressive Strength of Lightweight Ceramsite Aggregate UHPC Using Boosting and Bagging Techniques
Md. Sohel Rana, Fangyuan Li
Materials Today Communications (2024), pp. 110759-110759
Closed Access | Times Cited: 3
Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm
Sanjog Chhetri Sapkota, Sagar Sapkota, Gaurav Saini
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 4343-4359
Closed Access | Times Cited: 2
Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP)
Yunfeng Qian, Jianyu Yang, Weijun Yang, et al.
Buildings (2024) Vol. 14, Iss. 9, pp. 2675-2675
Open Access | Times Cited: 2
Prediction of Concrete Compressive Strength Based on ISSA-BPNN-AdaBoost
Ping Li, Zichen Zhang, Jiming Gu
Materials (2024) Vol. 17, Iss. 23, pp. 5727-5727
Open Access
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