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:
Innovative hybrid machine learning models for estimating the compressive strength of copper mine tailings concrete
Mana Alyami, Kennedy C. Onyelowe, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03869-e03869
Open Access | Times Cited: 5
Mana Alyami, Kennedy C. Onyelowe, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03869-e03869
Open Access | Times Cited: 5
Showing 5 citing articles:
AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry
Md. Habibur Rahman Sobuz, Md. Abu Saleh, Md. Samiun, et al.
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 1, pp. 20529-20537
Open Access | Times Cited: 2
Md. Habibur Rahman Sobuz, Md. Abu Saleh, Md. Samiun, et al.
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 1, pp. 20529-20537
Open Access | Times Cited: 2
Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models
Irfan Ullah, Muhammad Faisal Javed, Hisham Alabduljabbar, et al.
Structures (2025) Vol. 71, pp. 108138-108138
Closed Access | Times Cited: 1
Irfan Ullah, Muhammad Faisal Javed, Hisham Alabduljabbar, et al.
Structures (2025) Vol. 71, pp. 108138-108138
Closed Access | Times Cited: 1
Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learning
Navaratnarajah Sathiparan
Sustainable Chemistry and Pharmacy (2025) Vol. 43, pp. 101915-101915
Closed Access
Navaratnarajah Sathiparan
Sustainable Chemistry and Pharmacy (2025) Vol. 43, pp. 101915-101915
Closed Access
AI-Powered Optimization of Engineered Cementitious Composites Properties and CO₂ Emissions for Sustainable Construction
Qiuying Chang, Chuanhai Zhao, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2025), pp. e04405-e04405
Open Access
Qiuying Chang, Chuanhai Zhao, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2025), pp. e04405-e04405
Open Access
Machine Learning-Based Partition Method for Cyclic Development Mode of Submarine Soil Martials from Offshore Wind Farms
Ben He, Mingbao Lin, Zhishuai Zhang, et al.
Journal of Marine Science and Engineering (2025) Vol. 13, Iss. 3, pp. 533-533
Open Access
Ben He, Mingbao Lin, Zhishuai Zhang, et al.
Journal of Marine Science and Engineering (2025) Vol. 13, Iss. 3, pp. 533-533
Open Access
Precision assessment of the machine learning tools for the strength optimization of environmental-friendly lightweight foam concrete
Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, et al.
Journal of Environmental Management (2024) Vol. 373, pp. 123462-123462
Closed Access
Muhammad Nasir Amin, Ayaz Ahmad, Kaffayatullah Khan, et al.
Journal of Environmental Management (2024) Vol. 373, pp. 123462-123462
Closed Access
