4.7 Article

A general spectral difference method for calculating the minimum safety distance to avoid the pounding of adjacent structures during earthquakes

期刊

ENGINEERING STRUCTURES
卷 150, 期 -, 页码 646-655

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2017.07.068

关键词

Adjacent building; Non-proportional damping; Seismic pounding; Safety distance; Pseudo-excitation method; Spectral difference method

资金

  1. High-speed rail joint fund [U1434204]
  2. CSC scholarship
  3. Graduate student research innovation project fund of Hunan province in China [CX2014B074]
  4. Special Fund of Strategic Leader in Central South University [2016CSU001]

向作者/读者索取更多资源

With the development of cities, buildings have become increasingly concentrated. Therefore, there are serious potential collision dangers between the adjacent buildings or the building partitions under earthquakes, hence buildings need to set the appropriate safety distance to avoid pounding. However, the calculation of the minimum safety distances (MSD) of the adjacent buildings in Chinese Code is rough and arbitrary, while the traditional response spectrum method is derived based on the assumption of proportional damping and it is difficult to consider the non-proportional damping characteristics for large and complex building systems. Based on the above, this paper proposes a new general spectral difference method (gSDM) to calculate the MSD. First, the pseudo-excitation method is used to derive the relative displacement random expression of adjacent buildings during earthquakes. Then, combined with the response spectrum method, the gSDM formula is established, in addition, the explicit expressions of combination coefficient in the gSDM formula are given. Finally, the examples of two adjacent multistorey shear-buildings are used to illustrate the accuracy and wide adaptability of the gSDM during the El-Centro earthquake. The results compared and discussed in detail with the classical spectral difference method (cSDM), SRSS method and time history analysis. (C) 2017 Elsevier Ltd. All rights reserved.

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