Collapse simulation of a super high-rise building subjected to extremely strong earthquakes

Lu, Xiao; Lu, Xinzheng; Wankai, Zhang; Ye, Lieping

doi:10.1007/s11431-011-4548-0

Cited by 75 publications

(55 citation statements)

References 12 publications

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“…In order to ensure the service life and security of constructional works, it is necessary to monitor the constructional work systematically during its building and operating. Due to the diversity, complexity and non-deterministic characters of inducing factors of the building subsidence deformation, the mechanical behavior and deformation trend of building subsidence also reflect non-linear characteristics with the coexistence of the uncertainty and randomness [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Existing methods for building subsidence deformation prediction usually establish the time series analysis model by utilizing monitored data [35][36][37][38][39][40].…”

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confidence: 99%

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Wang

Hou

et al. 2012

Chin. Sci. Bull.

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This paper describes a building subsidence deformation prediction model with the self-memorization principle. According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation, a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation. This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method, which treats the monitored time series data as particular solutions of the nonlinear dynamic system. Then, the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation. As the memory coefficients of the proposed model are calculated with historical data, which contain useful information for the prediction and overcome the shortcomings of the average prediction, the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods. The model was applied to subsidence deformation prediction of a building in Xi'an. It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy. The fast development of economics promotes the urbanization of China. More and more high-rises appear in our cities [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In order to ensure the service life and security of constructional works, it is necessary to monitor the constructional work systematically during its building and operating. Due to the diversity, complexity and non-deterministic characters of inducing factors of the building subsidence deformation, the mechanical behavior and deformation trend of building subsidence also reflect non-linear characteristics with the coexistence of the uncertainty and randomness [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Existing methods for building subsidence deformation prediction usually establish the time series analysis model by utilizing monitored data [35][36][37][38][39][40]. However, most of these models are of the "parameter model" class which may not have clear physical meaning of parameters and discard effective parts of monitoring data. On the basis of the retrieved modeling, self-memorization principle has been recently developed based on the mechanism of time series data. As a mathematic realization on integration of deterministic and random theory, the self-memory model is a statistical-dynamical method to solve problems in nonlinear dynamic systems [41][42][43]. The core method of the model is to transform differential equations into difference-integral equations by introduction of the self-memory function. For dynamic systems described by differential equations, pre...

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confidence: 99%

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Wang

Hou

et al. 2012

Chin. Sci. Bull.

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“…The capacity and accuracy of THUFIBER is discussed in detail in Ye et al (2006) , Wang et al (2007) and Li et al(2011). THFIBER has excellent convergence performance in applications of strong nonlinear problems such as collapse (Li et al, 2011;Lu et al, 2011). In the analyses, the following collapse criterion is used: the structure deformed so Inter-story drift ratio and shear capacity of joints large so that the structure is unable further to provide enough free space for life safety.…”

Section: Collapse Fragility Analysis Based On Idamentioning

confidence: 99%

Evaluation of collapse resistance of RC frame structures for Chinese schools in seismic design categories B and C

Bao-xin

et al. 2011

Earthq. Eng. Eng. Vib.

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According to the Code for Seismic Design of Buildings (GB50011-2001), ten typical reinforced concrete (RC) frame structures, used as school classroom buildings, are designed with different seismic fortifi cation intensities (SFIs) (SFI=6 to 8.5) and different seismic design categories (SDCs) (SDC=B and C). The collapse resistance of the frames with SDC=B and C in terms of collapse fragility curves are quantitatively evaluated and compared via incremental dynamic analysis (IDA). The results show that the collapse resistance of structures should be evaluated based on both the absolute seismic resistance and the corresponding design seismic intensity. For the frames with SFI from 6 to 7.5, because they have relatively low absolute seismic resistance, their collapse resistance is insuffi cient even when their corresponding SDCs are upgraded from B to C. Thus, further measures are needed to enhance these structures, and some suggestions are proposed.

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“…Research to date indicates that numerical simulation is effective for nonlinear seismic analyses of such buildings [1][2][3][4][5][6][7][8][9][10][11], but most of the simulations were performed using various commercial finite element (FE) software packages. For example, based on the commercial FE code of MSC.Marc [12], Lu et al [1][2][3] proposed a numerical model integrating fiber-beam elements, multi-layer shell elements and an element deactivation technique for collapse simulation of super-tall buildings induced by extreme earthquakes. Poon et al [8] and Jiang et al [9] used Perform 3D, a commercial software package widely used for the design and nonlinear analysis of structures, to evaluate the performance of super-tall buildings in earthquakes.…”

Section: Introductionmentioning

confidence: 99%

A shear wall element for nonlinear seismic analysis of super-tall buildings using OpenSees

Xie

Guan

et al. 2015

Finite Elements in Analysis and Design

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282

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a b s t r a c tNumerical simulation has increasingly become an effective method and powerful tool for performancebased earthquake engineering research. Amongst the existing research efforts, most numerical analyses were conducted using general-purpose commercial software, which to some extent limits in-depth investigations on specific topics with complicated nature. In consequence, this work develops a new shear wall element model and associated material constitutive models based on the open source finite element (FE) code OpenSees, in order to perform nonlinear seismic analyses of high-rise RC frame-core tube structures. A series of shear walls, a 141.8-m frame-core tube building and a super-tall building (the Shanghai Tower, with a height of 632 m) are simulated. The rationality and reliability of the proposed element model and analysis method are validated through comparison with the available experimental data as well as the analytical results of a well validated commercial FE code. The research outcome will assist in providing a useful reference and an effective tool for further numerical analysis of the seismic behavior of tall and super-tall buildings.

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Collapse simulation of a super high-rise building subjected to extremely strong earthquakes

Cited by 75 publications

References 12 publications

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

Evaluation of collapse resistance of RC frame structures for Chinese schools in seismic design categories B and C

A shear wall element for nonlinear seismic analysis of super-tall buildings using OpenSees

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