prikaz prve stranice dokumenta Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization
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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

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Hadzima Nyarko, M. i 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 i 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, br. 2, 2022, str. 9-21. https://doi.org/10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko, Marijana i 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, br. 2 (2022): 9-21. https://doi.org/10.58845/jstt.utt.2022.en.2.9-21

Hadzima Nyarko, M. i 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), str. 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. [pristupljeno 10.01.2025.];1(2):9-21. doi: 10.58845/jstt.utt.2022.en.2.9-21

M. Hadzima Nyarko i 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, br. 2, str. 9-21, 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:326521. [Citirano: 10.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:326521
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Naslov (engleski)Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization
AutorMarijana Hadzima-Nyarko
AutorSon Hoang Trinh
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaTạp chí điện tử Khoa học và Công nghệ Giao thông = Journal of Science and Transport Technology
Brojčani podacivol. 1, br. 2, str. 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
Datum objave publikacije2022
URL dokumentahttps://jstt.vn/index.php/en/article/view/55
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-06 11:27:57