In this paper, the seismic resistance of unreinforced masonry (URM) cylindrical columns is investigated with an equivalent static analysis procedure. To this end, an existing numerical model developed for the stability analysis of masonry elements with rectangular cross-section is utilized and modified for the cylindrical columns. In the numerical model which takes into account the cracking of the sections and the second-order effects, the columns are divided ideally into sufficiently high number of elements, each having uniform curvature. The columns are modeled as prismatic cantilevers undergoing their own weights, eccentric vertical loads and distributed and concentrated static horizontal loads equivalent to the inertia actions. By considering two examples of columns, firstly a reference column and secondly a column from a real building, lateral seismic coefficient versus top drift level curves are obtained. On the basis of these curves, lateral load behavior of the columns is interpreted and maximum seismic load values which can be resisted by each column are determined. Implementing parametric analyses on the reference column, sensitivity of the seismic resistance to parameters such as column slenderness, magnitude and eccentricity of vertical top load, and the flexibility parameter is determined. The influence of some structural imperfections such as the deviation from vertical on the seismic resistance is also discussed in the paper.
Betonarme yüksek minarelerin rüzgâr yüklerine karşı dinamik davranışlarının iyileştirilmesi Ayarlı kütle sönümleyicilerin tasarımı Yapıların sonlu elemanlar yöntemiyle zaman tanım alanında dinamik analizi
In this paper, buckling analysis of slender prismatic columns with a single non-propagating open edge crack subjected to axial loads has been presented utilizing the transfer matrix method and the artificial neural networks. A multi-layer feedforward neural network learning by backpropagation algorithm has been employed in the study. The main focus of this work is the investigation of feasibility of using an artificial neural network to assess the critical buckling load of axially loaded compression rods. This is explored by comparing the performance of neural network models with the results of the matrix method for all considered support conditions. It can be seen from the results that the critical buckling load values obtained from the neural networks closely follow the values obtained from the matrix method for the whole data sets. The final results show that the proposed methodology may constitute an efficient tool for the estimation of elastic buckling loads of edge-cracked columns. Also, it can be seen from the results that the computational time reduces if the proposed method is used.
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