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:
Predict the compressive strength of ultra high-performance concrete by a hybrid method of machine learning
Nana Gong, Naimin Zhang
Journal of Engineering and Applied Science (2023) Vol. 70, Iss. 1
Open Access | Times Cited: 8
Nana Gong, Naimin Zhang
Journal of Engineering and Applied Science (2023) Vol. 70, Iss. 1
Open Access | Times Cited: 8
Showing 8 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: 49
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 49
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
Yaren Aydın, Celal Çakıroğlu, Gebrai̇l Bekdaş, et al.
Biomimetics (2024) Vol. 9, Iss. 9, pp. 544-544
Open Access | Times Cited: 5
Strength and durability predictions of ternary blended nano-engineered high-performance concrete: Application of hybrid machine learning techniques with bio-inspired optimization
Vikrant S. Vairagade, Boskey V. Bahoria, Haytham F. Isleem, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110470-110470
Closed Access
Vikrant S. Vairagade, Boskey V. Bahoria, Haytham F. Isleem, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 148, pp. 110470-110470
Closed Access
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
Bryan K. Aylas-Paredes, Taihao Han, Advaith Neithalath, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Prediction and deployment of compressive strength of high-performance concrete using ensemble learning techniques
Ridwan Taiwo, Abdul‐Mugis Yussif, Adesola Habeeb Adegoke, et al.
Construction and Building Materials (2024) Vol. 451, pp. 138808-138808
Closed Access | Times Cited: 4
Ridwan Taiwo, Abdul‐Mugis Yussif, Adesola Habeeb Adegoke, et al.
Construction and Building Materials (2024) Vol. 451, pp. 138808-138808
Closed Access | Times Cited: 4
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: 3
Dingqiang Fan, Ziao Chen, Yuan Cao, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108555-108555
Closed Access | Times Cited: 3
Investigation of mechanical properties of high-performance concrete via multi-method of regression tree approach
Rui Qi, Haiyan Wu, Yongjun Qi, et al.
Materials Today Communications (2024) Vol. 40, pp. 109922-109922
Closed Access | Times Cited: 1
Rui Qi, Haiyan Wu, Yongjun Qi, et al.
Materials Today Communications (2024) Vol. 40, pp. 109922-109922
Closed Access | Times Cited: 1
Using Multiple Machine Learning Models to Predict the Strength of UHPC Mixes with Various FA Percentages
Hussam Safieh, Rami A. Hawileh, Maha Assad, et al.
Infrastructures (2024) Vol. 9, Iss. 6, pp. 92-92
Open Access
Hussam Safieh, Rami A. Hawileh, Maha Assad, et al.
Infrastructures (2024) Vol. 9, Iss. 6, pp. 92-92
Open Access
