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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:

Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques
Omar Abuodeh, Jamal A. Abdalla, Rami A. Hawileh
Applied Soft Computing (2020) Vol. 95, pp. 106552-106552
Closed Access | Times Cited: 164

Showing 1-25 of 164 citing articles:

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: 365
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: 205
Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices
Arash Teymori Gharah Tapeh, M.Z. Naser
Archives of Computational Methods in Engineering (2022) Vol. 30, Iss. 1, pp. 115-159
Closed Access | Times Cited: 153
Efficient estimating compressive strength of ultra-high performance concrete using XGBoost model
Ngoc-Hien Nguyen, Joaquín Abellán García, Seunghye Lee, et al.
Journal of Building Engineering (2022) Vol. 52, pp. 104302-104302
Closed Access | Times Cited: 103
Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms
Torkan Shafighfard, Faramarz Bagherzadeh, Rana Abdollahi Rizi, et al.
Journal of Materials Research and Technology (2022) Vol. 21, pp. 3777-3794
Open Access | Times Cited: 86
Machine learning-based compressive strength modelling of concrete incorporating waste marble powder
Elyas Asadi Shamsabadi, Naeim Roshan, S.A. Hadigheh, et al.
Construction and Building Materials (2022) Vol. 324, pp. 126592-126592
Closed Access | Times Cited: 80
Intelligent design and manufacturing of ultra-high performance concrete (UHPC) – A review
Dingqiang Fan, Jinyun Zhu, Mengxin Fan, et al.
Construction and Building Materials (2023) Vol. 385, pp. 131495-131495
Closed Access | Times Cited: 73
Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms
Vimal Rathakrishnan, Salmia Beddu, Ali Najah Ahmed
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 69
Comparison of various machine learning algorithms used for compressive strength prediction of steel fiber-reinforced concrete
Seyed Soroush Pakzad, Naeim Roshan, Mansour Ghalehnovi
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 50
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: 48
Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 109053-109053
Closed Access | Times Cited: 47
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
Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures
Rayed Alyousef, Muhammad Faisal Rehman, Majid Khan, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02418-e02418
Open Access | Times Cited: 40
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: 40
Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete
Emadaldin Mohammadi Golafshani, Ali Behnood, Taehwan Kim, et al.
Applied Soft Computing (2024) Vol. 159, pp. 111661-111661
Open Access | Times Cited: 16
Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms
Metin Katlav, Faruk Ergen
Structural Concrete (2024)
Open Access | Times Cited: 14
Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review
Rakesh Kumar, Baboo Rai, Pijush Samui
Structural Concrete (2024)
Closed Access | Times Cited: 13
Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks
Afshin Marani, Armin Jamali, Moncef L. Nehdi
Materials (2020) Vol. 13, Iss. 21, pp. 4757-4757
Open Access | Times Cited: 111
Estimating compressive strength of modern concrete mixtures using computational intelligence: A systematic review
Itzel Nunez, Afshin Marani, Majdi Flah, et al.
Construction and Building Materials (2021) Vol. 310, pp. 125279-125279
Closed Access | Times Cited: 84
Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach
Hongwei Song, Ayaz Ahmad, Krzysztof Ostrowski, et al.
Materials (2021) Vol. 14, Iss. 16, pp. 4518-4518
Open Access | Times Cited: 74
Design of sustainable ultra-high performance concrete: A review
Xinpeng Wang, Di Wu, Jinrui Zhang, et al.
Construction and Building Materials (2021) Vol. 307, pp. 124643-124643
Closed Access | Times Cited: 72
Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques
Yue Xu, Waqas Ahmad, Ayaz Ahmad, et al.
Materials (2021) Vol. 14, Iss. 22, pp. 7034-7034
Open Access | Times Cited: 65
Predicting load capacity of shear walls using SVR–RSM model
Behrooz Keshtegar, Moncef L. Nehdi, T. Nguyen‐Thoi, et al.
Applied Soft Computing (2021) Vol. 112, pp. 107739-107739
Closed Access | Times Cited: 57
Predicting shear strength of FRP-reinforced concrete beams using novel synthetic data driven deep learning
Afshin Marani, Moncef L. Nehdi
Engineering Structures (2022) Vol. 257, pp. 114083-114083
Closed Access | Times Cited: 49
Prediction on compressive strength of Engineered Cementitious composites using Machine learning approach
N. Shanmugasundaram, S. Praveenkumar, K. Gayathiri, et al.
Construction and Building Materials (2022) Vol. 342, pp. 127933-127933
Closed Access | Times Cited: 41
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