prikaz prve stranice dokumenta Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation
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Scientific paper - Original scientific paper
Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation
Buildings, 9 (2019), 5; 121. https://doi.org/10.3390/buildings9050121
Kalman Šipoš, Tanja; Strukar, Kristina

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Kalman Šipoš, T. & Strukar, K. (2019). Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation. Buildings, 9. (5). doi: 10.3390/buildings9050121

Kalman Šipoš, Tanja and Kristina Strukar. "Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation." Buildings, vol. 9, no. 5, 2019. https://doi.org/10.3390/buildings9050121

Kalman Šipoš, Tanja and Kristina Strukar. "Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation." Buildings 9, no. 5 (2019). https://doi.org/10.3390/buildings9050121

Kalman Šipoš, T. and Strukar, K. (2019) 'Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation', Buildings, 9(5). doi: 10.3390/buildings9050121

Kalman Šipoš T, Strukar K. Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation. Buildings [Internet]. 2019 [cited 2024 December 23];9(5). doi: 10.3390/buildings9050121

T. Kalman Šipoš and K. Strukar, "Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation", Buildings, vol. 9, no. 5, 2019. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:773124. [Accessed: 23 December 2024]

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Metadata
Title (english)Prediction of the Seismic Response of Multi- Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation
AuthorTanja Kalman Šipoš
AuthorKristina Strukar
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 order to test the reliability of neural networks for the prediction of the behaviour of multi-storey multi-bay infilled frames, neural network processing was done on an experimental database of one-storey one-bay reinforced- concrete (RC) frames with masonry infills. From the obtained results it is demonstrated that they are acceptable for the prediction of base shear (BS) and inter-storey drift ratios (IDR) in characteristic points of the primary curve of infilled frame behaviour under seismic loads. The results obtained on one-storey one-bay infilled frames was extended to multi-bay infilled frames by evaluating and comparing numerical finite element modelling(FEM) modelling and neural network results with suggested approximating equations for the definition of bilinear capacity by defined BS and IDRs. The main goal of this paper is to offer an interpretation of the behaviour of multi-storey multi-bay masonry infilled frames according to a bilinear capacity curve, and to present the infilled frame’s response according to the contributions of frame and infill. The presented methodology is validated by experimental results from multi-storey multi-bay masonry infilled frames.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleBuildings
Numberingvol. 9, no. 5, 121
p-ISSN2075-5309
DOIhttps://doi.org/10.3390/buildings9050121
URN:NBNurn:nbn:hr:133:773124
Publication2019
Document URLhttps://www.mdpi.com/2075-5309/9/5/121
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-03 09:15:26