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:

Artificial intelligence-based estimation of ultra-high-strength concrete's flexural property
Qichen Wang, Abasal Hussain, Muhammad Usman Farooqi, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01243-e01243
Open Access | Times Cited: 56

Showing 1-25 of 56 citing articles:

Recent developments on natural fiber concrete: A review of properties, sustainability, applications, barriers, and opportunities
Lin Chen, Zhonghao Chen, Zhuolin Xie, et al.
Developments in the Built Environment (2023) Vol. 16, pp. 100255-100255
Open Access | Times Cited: 83
Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 65
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
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: 48
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 19
Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete
Mohamed Abdellatief, G. Murali, Saurav Dixit
Results in Engineering (2025) Vol. 25, pp. 104542-104542
Open Access | Times Cited: 7
Data-driven based estimation of waste-derived ceramic concrete from experimental results with its environmental assessment
Qiuying Chang, Lanlan Liu, Muhammad Usman Farooqi, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 6348-6368
Open Access | Times Cited: 42
Revolutionizing concrete analysis: An in-depth survey of AI-powered insights with image-centric approaches on comprehensive quality control, advanced crack detection and concrete property exploration
Kaustav Sarkar, Amit Shiuly, Krishna Gopal Dhal
Construction and Building Materials (2023) Vol. 411, pp. 134212-134212
Closed Access | Times Cited: 37
Modeling the chloride migration of recycled aggregate concrete using ensemble learners for sustainable building construction
Emadaldin Mohammadi Golafshani, Alireza Kashani, Ali Behnood, et al.
Journal of Cleaner Production (2023) Vol. 407, pp. 136968-136968
Closed Access | Times Cited: 30
Application of deep learning in damage classification of reinforced concrete bridges
Mustafa Abubakr, Mohammed Rady, Khaled Badran, et al.
Ain Shams Engineering Journal (2023) Vol. 15, Iss. 1, pp. 102297-102297
Open Access | Times Cited: 27
Machine and Deep Learning Methods for Concrete Strength Prediction: A Bibliometric and Content Analysis Review of Research Trends and Future Directions
Raman Kumar, Essam Althaqafi, S. Gopal Krishna Patro, et al.
Applied Soft Computing (2024) Vol. 164, pp. 111956-111956
Closed Access | Times Cited: 14
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete
Tariq Ali, Mohamed Hechmi El Ouni, Muhammad Zeeshan Qureshi, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137370-137370
Closed Access | Times Cited: 11
Artificial intelligence for calculating and predicting building carbon emissions: a review
Jianmin Hua, Ruiyi Wang, Ying Cheng Hu, et al.
Environmental Chemistry Letters (2025)
Open Access | Times Cited: 1
Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Polymers (2022) Vol. 14, Iss. 15, pp. 3065-3065
Open Access | Times Cited: 38
Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms
Adil Khan, Majid Khan, Mohsin Ali, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02744-e02744
Open Access | Times Cited: 21
Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications
Duygu Ege, Seda Sertturk, Berk Acarkan, et al.
Biomedical Physics & Engineering Express (2023) Vol. 9, Iss. 6, pp. 065014-065014
Open Access | Times Cited: 20
The influence of nano-silica precursor on the compressive strength of mortar using Advanced Machine Learning for sustainable buildings
Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 2, pp. 1135-1148
Closed Access | Times Cited: 19
A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete
Jinpeng Feng, Haowei Zhang, Kang Gao, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01653-e01653
Open Access | Times Cited: 26
Early-age compressive strength prediction of cemented phosphogypsum backfill using lab experiments and ensemble learning models
Chendi Min, Xiong Shuai, Ying Shi, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02107-e02107
Open Access | Times Cited: 16
Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete
Yunfeng Qian, Muhammad Sufian, Ahmad Hakamy, et al.
Frontiers in Materials (2023) Vol. 9
Open Access | Times Cited: 15
Predicting parameters and sensitivity assessment of nano-silica-based fiber-reinforced concrete: a sustainable construction material
Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sufian, et al.
Journal of Materials Research and Technology (2023) Vol. 23, pp. 3943-3960
Open Access | Times Cited: 15
Novel ANOVA-Statistic-Reduced Deep Fully Connected Neural Network for the Damage Grade Prediction of Post-Earthquake Buildings
K. R. Sri Preethaa, M. Shyamala Devi, Aruna Rajendran, et al.
Sensors (2023) Vol. 23, Iss. 14, pp. 6439-6439
Open Access | Times Cited: 14
Competitive study of a geothermal heat pump equipped with an intermediate economizer for various ORC working fluids
Zhao Song, Azher M. Abed, Ahmed Farouk Deifalla, et al.
Case Studies in Thermal Engineering (2023) Vol. 45, pp. 102954-102954
Open Access | Times Cited: 13
Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications
Burak Koçak, İbrahim Pınarcı, Uğur Güvenç, et al.
Construction and Building Materials (2023) Vol. 385, pp. 131516-131516
Closed Access | Times Cited: 13
Application of machine learning algorithm in the internal and external hazards from industrial byproducts
Solomon Oyebisi, H. I. Owamah, Maxwell Omeje
Cleaner Engineering and Technology (2023) Vol. 13, pp. 100629-100629
Open Access | Times Cited: 11
Page 1 - Next Page

Scroll to top