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Open AccessArticle

Prediction of the Seismic Response of Multi-Storey Multi-Bay Masonry Infilled Frames Using Artificial Neural Networks and a Bilinear Approximation

Faculty of Civil Engineering and Architecture Osijek, Department for Technical Mechanics, 31000 Osijek, Croatia
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Buildings 2019, 9(5), 121; https://doi.org/10.3390/buildings9050121
Received: 30 March 2019 / Revised: 7 May 2019 / Accepted: 9 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Masonry Buildings: Research and Practice)
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. View Full-Text
Keywords: masonry; infilled frames; capacity curve; bilinear approximation; neural networks; database masonry; infilled frames; capacity curve; bilinear approximation; neural networks; database
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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 2019, 9, 121.

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