ASSESSMENT OF THE RESPONSE REDUCTION FACTORS OF PLAN-IRREGULAR RC BUILDINGS

Inter-storey drift is an important parameter of structural behavior in seismic analysis of buildings. Pounding effect in building simply means collision between adjacent buildings due to earthquake load caused by out of phase vibration of adjacent buildings. There is variation in inter-storey drift of adjacent buildings during pounding case and no pounding case. The main objective of this research was to compare the inter-storey drift of general adjacent RC buildings in pounding and no pounding case. For this study two adjacent RC buildings having same number of stories have been considered. For pounding case analysis there is no gap in between adjacent buildings and for no pounding case analysis there is sufficient distance between adjacent buildings. The model consists of adjacent buildings having 4 and 4 stories but unequal storey height. Both the buildings have same material & sectional properties. Fast non-linear time history analysis was performed by using El-centro earthquake data as ground motion. Adjacent buildings having different overall height were modelled in SAP 2000 v 15 using gap element for pounding case. Finally, analysis was done and inter-storey drift was compared. It was found that in higher building inter-storey drift is greater in no pounding case than in pounding case but in adjacent lower height building the result was reversed. Additionally, it was found that in general residential RC buildings maximum inter-storey drift occurs in 2nd floor.

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A novel approach for the evaluation of the economical losses due to seismic actions on RC buildings with masonry infills

Soil Dynamics and Earthquake Engineering ◽

10.1016/j.soildyn.2021.106722 ◽

2021 ◽

Vol 145 ◽

pp. 106722

Author(s):

A. Rossi ◽

P. Morandi ◽

G. Magenes

Keyword(s):

Rc Buildings ◽

Masonry Infills ◽

Novel Approach ◽

Economical Losses

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Influence of floor system on seismic behavior of RC buildings to forward directivity and fling-step in the near-fault region

Structures ◽

10.1016/j.istruc.2021.01.052 ◽

2021 ◽

Vol 30 ◽

pp. 803-817

Author(s):

Sayed Mahmoud ◽

Ali Alqarni ◽

Joseph Saliba ◽

Amal H. Ibrahim ◽

Magdy genidy ◽

...

Keyword(s):

Seismic Behavior ◽

Rc Buildings ◽

Near Fault ◽

Forward Directivity ◽

Fling Step ◽

Floor System ◽

Fault Region

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Basement-Storey Effect on the Seismic Response of RC Buildings on Soft Surface Soil

Arabian Journal for Science and Engineering ◽

10.1007/s13369-021-05714-4 ◽

2021 ◽

Author(s):

Naci Çağlar ◽

Sedat Sert ◽

Ahmet Hamdi Serdar

Keyword(s):

Seismic Response ◽

Surface Soil ◽

Rc Buildings ◽

Soft Surface

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Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

Scientific Reports ◽

10.1038/s41598-021-88567-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jinhua Sheng ◽

Yuchen Shi ◽

Qiao Zhang

Keyword(s):

Magnetic Resonance Imaging ◽

Variable Density ◽

Reduction Factor ◽

Resonance Imaging ◽

Parallel Magnetic Resonance Imaging ◽

Multiple Variable ◽

Space Data ◽

Sampling Pattern ◽

Undersampled Data ◽

Reduction Factors

AbstractGeneralized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

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