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

Tanja Kalman Šipoš,Kristina Strukar

doi:10.3390/buildings9050121

Abstract

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.

Highlights

The use of masonry infilled frames is very common for most types of building, state of the art of masonry infilled frame behaviour [1,2,3] in general is known but there is still no suggestion of regulations on how to model or use it properly in structural analysis.The use of neural networks in the civil engineering field is already approved [4,5] the application of neural networks for the prediction of infilled frame behaviour is rare
It was limited to only one-storey, one-bay infilled frames according to the availability and uniformity of the structural type
Figure 6modelling the force-displacement curves for reinforced concrete frames without infills

Summary

Introduction

The use of masonry infilled frames is very common for most types of building, state of the art of masonry infilled frame behaviour [1,2,3] in general is known but there is still no suggestion of regulations on how to model or use it properly in structural analysis. The use of neural networks in the civil engineering field is already approved [4,5] the application of neural networks for the prediction of infilled frame behaviour is rare. With a lack of available data from experiments of masonry infilled frames and with the uncertainty of numerical modelling, this research area needs to be further investigated. In order to connect most of the previously published data with new valuable conclusions, an experimental database of masonry infilled frames was collected. It was limited to only one-storey, one-bay infilled frames according to the availability and uniformity of the structural type

Objectives

Methods

Results

Conclusion