Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm
Qinghua Han, Changqing Gui, Jie Xu, et al.
Construction and Building Materials (2019) Vol. 226, pp. 734-742
Closed Access | Times Cited: 327

Showing 1-25 of 327 citing articles:

Machine learning prediction of mechanical properties of concrete: Critical review
Wassim Ben Chaabene, Majdi Flah, Moncef L. Nehdi
Construction and Building Materials (2020) Vol. 260, pp. 119889-119889
Closed Access | Times Cited: 560
Machine learning for structural engineering: A state-of-the-art review
Huu‐Tai Thai
Structures (2022) Vol. 38, pp. 448-491
Closed Access | Times Cited: 414
Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners
Furqan Farooq, Wisal Ahmed, Arslan Akbar, et al.
Journal of Cleaner Production (2021) Vol. 292, pp. 126032-126032
Closed Access | Times Cited: 323
A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)
I.U. Ekanayake, D.P.P. Meddage, Upaka Rathnayake
Case Studies in Construction Materials (2022) Vol. 16, pp. e01059-e01059
Open Access | Times Cited: 260
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105444-105444
Open Access | Times Cited: 246
A Comparative Study of Random Forest and Genetic Engineering Programming for the Prediction of Compressive Strength of High Strength Concrete (HSC)
Furqan Farooq, Muhammad Nasir Amin, Kaffayatullah Khan, et al.
Applied Sciences (2020) Vol. 10, Iss. 20, pp. 7330-7330
Open Access | Times Cited: 229
Machine learning in concrete science: applications, challenges, and best practices
Zhanzhao Li, Jinyoung Yoon, Rui Zhang, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 197
Applications of Gene Expression Programming for Estimating Compressive Strength of High‐Strength Concrete
Fahid Aslam, Furqan Farooq, Muhammad Nasir Amin, et al.
Advances in Civil Engineering (2020) Vol. 2020, Iss. 1
Open Access | Times Cited: 185
Compressive Strength of Fly‐Ash‐Based Geopolymer Concrete by Gene Expression Programming and Random Forest
Mohsin Ali Khan, Shazim Ali Memon, Furqan Farooq, et al.
Advances in Civil Engineering (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 181
Machine learning study of the mechanical properties of concretes containing waste foundry sand
Ali Behnood, Emadaldin Mohammadi Golafshani
Construction and Building Materials (2020) Vol. 243, pp. 118152-118152
Closed Access | Times Cited: 171
Predicting the compressive strength of concrete containing metakaolin with different properties using ANN
Mohammad Javad Moradi, Mohsen Khaleghi, Javid Salimi, et al.
Measurement (2021) Vol. 183, pp. 109790-109790
Closed Access | Times Cited: 152
Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Yanqi Wu, Yisong Zhou
Construction and Building Materials (2022) Vol. 330, pp. 127298-127298
Closed Access | Times Cited: 139
Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning
Gideon A. Lyngdoh, Mohd Zaki, N. M. Anoop Krishnan, et al.
Cement and Concrete Composites (2022) Vol. 128, pp. 104414-104414
Open Access | Times Cited: 136
Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm
Li Hong, Jiajian Lin, Xiaobao Lei, et al.
Materials Today Communications (2022) Vol. 30, pp. 103117-103117
Closed Access | Times Cited: 112
Predictive modeling of compressive strength of sustainable rice husk ash concrete: Ensemble learner optimization and comparison
Bawar Iftikhar, Sophia C. Alih, Mohammadreza Vafaei, et al.
Journal of Cleaner Production (2022) Vol. 348, pp. 131285-131285
Closed Access | Times Cited: 109
Mechanoluminescent-Triboelectric Bimodal Sensors for Self-Powered Sensing and Intelligent Control
Bo Zhou, Jize Liu, Xin Huang, et al.
Nano-Micro Letters (2023) Vol. 15, Iss. 1
Open Access | Times Cited: 83
Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning
Zhongjie Shen, Ahmed Farouk Deifalla, Paweł Kamiński, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3523-3523
Open Access | Times Cited: 76
Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete
Xiongzhou Yuan, Yuze Tian, Waqas Ahmad, et al.
Materials (2022) Vol. 15, Iss. 8, pp. 2823-2823
Open Access | Times Cited: 73
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
A review of the trends, evolution, and future research prospects of hydrogen fuel cells – A focus on vehicles
Ephraim Bonah Agyekum, Flavio Odoi-Yorke, Agnes Abeley Abbey, et al.
International Journal of Hydrogen Energy (2024) Vol. 72, pp. 918-939
Closed Access | Times Cited: 57
Predicting compressive strength of geopolymer concrete using machine learning
Priyanka Gupta, Nakul Gupta, Kuldeep K. Saxena
Innovation and Emerging Technologies (2023) Vol. 10
Closed Access | Times Cited: 55
Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Mana Alyami, Majid Khan, Muhammad Fawad, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02728-e02728
Open Access | Times Cited: 55
Predicting ultra-high-performance concrete compressive strength using gene expression programming method
Hisham Alabduljabbar, Majid Khan, Hamad Hassan Awan, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02074-e02074
Open Access | Times Cited: 53
A comparative study of prediction of compressive strength of ultra‐high performance concrete using soft computing technique
Rakesh Kumar, Baboo Rai, Pijush Samui
Structural Concrete (2023) Vol. 24, Iss. 4, pp. 5538-5555
Closed Access | Times Cited: 52
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: 39
Page 1 - Next Page

Scroll to top