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 machine learning approach for assessing the compressive strength of cementitious composites reinforced by graphene derivatives
Arman Montazerian, Mohammad Hajmohammadian Baghban, Raghavendra Ramachandra, et al.
Construction and Building Materials (2023) Vol. 409, pp. 134014-134014
Open Access | Times Cited: 6
Arman Montazerian, Mohammad Hajmohammadian Baghban, Raghavendra Ramachandra, et al.
Construction and Building Materials (2023) Vol. 409, pp. 134014-134014
Open Access | Times Cited: 6
Showing 6 citing articles:
Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Majid Khan, Roz‐Ud‐Din Nassar, Waqar Anwar, et al.
Results in Engineering (2024) Vol. 21, pp. 101837-101837
Open Access | Times Cited: 28
Majid Khan, Roz‐Ud‐Din Nassar, Waqar Anwar, et al.
Results in Engineering (2024) Vol. 21, pp. 101837-101837
Open Access | Times Cited: 28
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 21
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 21
Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches
Rayed Alyousef, Roz‐Ud‐Din Nassar, Muhammad Fawad, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03018-e03018
Open Access | Times Cited: 12
Rayed Alyousef, Roz‐Ud‐Din Nassar, Muhammad Fawad, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03018-e03018
Open Access | Times Cited: 12
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 7
Adsorption Capacity of Graphane Materials Prediction using Modified Artificial Neural Network
Youliang Ma, Kejuan Qiang
(2024), pp. 1-4
Closed Access
Youliang Ma, Kejuan Qiang
(2024), pp. 1-4
Closed Access
Exploring the predictive potential of artificial neural networks in enhancing mechanical properties of derivatives of graphene nanocomposites: a data-driven approach
D. Nageswara Rao, Mamta Dahiya, Amit Kumar Arora, et al.
International Journal of Information Technology (2024)
Closed Access
D. Nageswara Rao, Mamta Dahiya, Amit Kumar Arora, et al.
International Journal of Information Technology (2024)
Closed Access
