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:

Showing 19 citing articles:

Coupled extreme gradient boosting algorithm with artificial intelligence models for predicting compressive strength of fiber reinforced polymer- confined concrete
Tao Hai, Zainab Hasan Ali, Faisal M. Mukhtar, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 134, pp. 108674-108674
Closed Access | Times Cited: 9
Comparative use of different AI methods for the prediction of concrete compressive strength
Mouhamadou Amar
Cleaner Materials (2025) Vol. 15, pp. 100299-100299
Open Access | Times Cited: 1
AutoGluon-enabled machine learning models for predicting recycled aggregate concrete’s compressive strength
Chukwuemeka Daniel
Australian Journal of Structural Engineering (2025), pp. 1-15
Closed Access | Times Cited: 1
Predicting compressive strength of sustainable concrete using advanced AI models: DLNN, RF, and MARS
Monali Wagh, Charuta Waghmare, Amit Gudadhe, et al.
Asian Journal of Civil Engineering (2025)
Closed Access | Times Cited: 1
Deep learning based concrete compressive strength prediction model with hybrid meta-heuristic approach
Deepa A. Joshi, Radhika Menon, R.K. Jain, et al.
Expert Systems with Applications (2023) Vol. 233, pp. 120925-120925
Closed Access | Times Cited: 23
Study on predicting compressive strength of concrete using supervised machine learning techniques
B. Vamsi Varma, Elluri Venkata Prasad, Sudhakar Singha
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 7, pp. 2549-2560
Closed Access | Times Cited: 22
Applications of artificial intelligence in the AEC industry: a review and future outlook
Huimin Li, Yafei Zhang, Yongchao Cao, et al.
Journal of Asian Architecture and Building Engineering (2024), pp. 1-17
Open Access | Times Cited: 7
Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
Nashat S. Alghrairi, Farah Nora Aznieta Abdul Aziz, Suraya Abdul Rashid, et al.
Open Engineering (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Prediction of compressive strength of nano-silica concrete by using random forest algorithm
Mayank Nigam, Manvendra Verma
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 7, pp. 5205-5213
Closed Access | Times Cited: 7
Hybrid machine learning approach for construction cost estimation: an evaluation of extreme gradient boosting model
Zainab Hasan Ali, Abbas M. Burhan
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 7, pp. 2427-2442
Closed Access | Times Cited: 14
Developing an Integrative Data Intelligence Model for Construction Cost Estimation
Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, et al.
Complexity (2022) Vol. 2022, Iss. 1
Open Access | Times Cited: 19
Modelling of Marshall Stability of Plastic-Reinforced Asphalt Concrete Using Machine Learning Algorithms and SHAP
M. Jamil, Ravi Jagirdar, Abul Kashem, et al.
Hybrid Advances (2025), pp. 100483-100483
Open Access
A Novel Pareto Front Symbiotic Organism Search (PF-SOS) Combined with Metaheuristic-Optimized Machine Learning for Optimal Recycled Aggregate Concrete Mixtures
Hendra Gunawan, John Thedy, Bagus Hario Setiadji, et al.
Journal of Building Engineering (2025), pp. 112991-112991
Closed Access
Chloride Permeability Coefficient Prediction of Rubber Concrete Based on the Improved Machine Learning Technical: Modelling and Performance Evaluation
Xiaoyu Huang, Shuai Wang, Tong Lu, et al.
Polymers (2023) Vol. 15, Iss. 2, pp. 308-308
Open Access | Times Cited: 9
Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
Saif Rehman, Raja Dilawar Riaz, Muhammad Usman, et al.
Applied Sciences (2024) Vol. 14, Iss. 16, pp. 7231-7231
Open Access | Times Cited: 2
Enhancing Sustainable Construction Materials Through the Integration of Generative Artificial Intelligence, such as ChatGPT or Bard
Nitin Liladhar Rane, Saurabh Choudhary, Jayesh Rane
SSRN Electronic Journal (2024)
Closed Access | Times Cited: 1
Predicting tensile strength of steel fiber-reinforced concrete based on a novel differential evolution-optimized extreme gradient boosting machine
Nhat‐Duc Hoang
Neural Computing and Applications (2024)
Closed Access | Times Cited: 1
Comparative Analysis of Automated Machine Learning and Optimized Conventional Machine Learning for Concrete’s Uniaxial Compressive Strength Prediction
Chukwuemeka Daniel
Advances in Civil Engineering (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 1
Applied AI and Robotics for Construction Operations—A Smart Review of the State of the Science
Anto Ovid, Abdullah Alsharef, S M Jamil Uddin, et al.
Construction Research Congress 2022 (2024), pp. 913-923
Closed Access
Metaheuristic-based machine learning approaches of compressive strength forecasting of steel fiber reinforced concrete with SHapley Additive exPlanations
Abul Kashem, Ayesha Anzer, Ravi Jagirdar, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access
Page 1

Scroll to top