prikaz prve stranice dokumenta Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks
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Scientific paper - Original scientific paper
Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks
Journal of soft computing in civil engineering, 4 (2020), 1; 111-126. https://doi.org/10.22115/SCCE.2020.221268.1181
Kalman Šipos, Tanja; Parsa, Payam

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Kalman Šipos, T. & Parsa, P. (2020). Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks. Journal of soft computing in civil engineering, 4. (1), 111-126. doi: 10.22115/SCCE.2020.221268.1181

Kalman Šipos, Tanja and Payam Parsa. "Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks." Journal of soft computing in civil engineering, vol. 4, no. 1, 2020, pp. 111-126. https://doi.org/10.22115/SCCE.2020.221268.1181

Kalman Šipos, Tanja and Payam Parsa. "Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks." Journal of soft computing in civil engineering 4, no. 1 (2020): 111-126. https://doi.org/10.22115/SCCE.2020.221268.1181

Kalman Šipos, T. and Parsa, P. (2020) 'Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks', Journal of soft computing in civil engineering, 4(1), pp. 111-126. doi: 10.22115/SCCE.2020.221268.1181

Kalman Šipos T, Parsa P. Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks. Journal of soft computing in civil engineering [Internet]. 2020 [cited 2024 December 27];4(1):111-126. doi: 10.22115/SCCE.2020.221268.1181

T. Kalman Šipos and P. Parsa, "Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks", Journal of soft computing in civil engineering, vol. 4, no. 1, pp. 111-126, 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:088066. [Accessed: 27 December 2024]

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Metadata
Title (english)Empirical Formulation of Ferrocement Members Moment Capacity Using Artificial Neural Networks
AuthorTanja Kalman Šipos
AuthorPayam Parsa
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)
In the two past decades, ferrocement members have been with a wide variety of uses in structural applications because of their unique physical properties (high surface-area-to-volume ratio and possible fabrication in any shape). In this study, two models were presented for a predict of the moment capacity of ferrocement members, one based on a back-propagation multilayer perceptron artificial neural network and the other proposing a new equation based on the multilayer perceptron network trained. These models with five input parameters including volume fraction of wire mesh, tensile strength, cube compressive strength of mortar, and width and the depth of specimens are presented. The results obtained from the two models are compared with experimental data and experimental equations such as plastic analysis, mechanism, and nonlinear regression approaches. Also, these results are compared with the results of the equations that researchers have proposed in recent years with soft computing methods (ANFIS, GEP, or GMDH). The prediction performance of the two models is significantly better than the experimental equations. These models are comparable to that of models provided with different soft computing methods to predict the moment capacity of ferrocement members. The result of this research has proposed a general equation with less mathematical complexity and more explicit.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleJournal of soft computing in civil engineering
Numberingvol. 4, no. 1, pp. 111-126
p-ISSN2588-2872
DOIhttps://doi.org/10.22115/SCCE.2020.221268.1181
URN:NBNurn:nbn:hr:133:088066
Publication2020
Document URLhttps://www.jsoftcivil.com/article_104916.html
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-04 09:30:37