prikaz prve stranice dokumenta Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization
  Download
HTML 3.54 KB
Scientific paper - Original scientific paper
Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization
Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology, 1 (2022), 2; 9-21. https://doi.org/10.58845/jstt.utt.2022.en.2.9-21
Hadzima-Nyarko, Marijana; Trinh, Son Hoang

Cite this document

Hadzima Nyarko, M. & Trinh, S. H. (2022). Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization. Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology, 1. (2), 9-21. doi: 10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko, Marijana and Son Hoang Trinh. "Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization." Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology, vol. 1, no. 2, 2022, pp. 9-21. https://doi.org/10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko, Marijana and Son Hoang Trinh. "Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization." Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology 1, no. 2 (2022): 9-21. https://doi.org/10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko, M. and Trinh, S. H. (2022) 'Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization', Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology, 1(2), pp. 9-21. doi: 10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko M, Trinh SH. Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization. Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology [Internet]. 2022 [cited 2025 January 14];1(2):9-21. doi: 10.58845/jstt.utt.2022.en.2.9-21

M. Hadzima Nyarko and S. H. Trinh, "Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization", Tạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology, vol. 1, no. 2, pp. 9-21, 2022. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:326521. [Accessed: 14 January 2025]

Please login to the repository to save this object to your list.
Metadata
Title (english)Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization
AuthorMarijana Hadzima-Nyarko
AuthorSon Hoang Trinh
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
Cement concrete is the most commonly used material today for constructing residential or commercial buildings, industrial parks, or particular components such as tunnel slabs where there is a high risk of fire. This structure requires concrete to be subjected to high temperatures generated by fires. However, concrete under the influence of high temperature has very complex behavior states with deformations, physical and chemical changes as the temperature rises dramatically. In this study, an artificial neural network-based Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition. The performance of the ANN model was evaluated using K-fold cross- validation and statistical criteria, including mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results show that the proposed ANN model is a reasonable, highly accurate, and useful prediction tool for saving time and minimizing costly experiments.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleTạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology
Numberingvol. 1, no. 2, pp. 9-21
p-ISSN2734-9942
e-ISSN2734-9950
DOIhttps://doi.org/10.58845/jstt.utt.2022.en.2.9-21
URN:NBNurn:nbn:hr:133:326521
Publication2022
Document URLhttps://jstt.vn/index.php/en/article/view/55
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-06 11:27:57