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The objective of this study was to investigate the effect of P-Delta analysis
on the static and dynamic stability analysis of high rise steel buildings
subjected to earthquake excitation. Different building models including ten,
twenty, thirty, forty and fifty story composite steel buildings with
symmetrical and asymmetrical layouts were analyzed....
Contexts in source publication
Context 1
... models due to real base excitation. In order to conduct time history dynamic analysis to simulate seismic base excitation, the building models were analyzed for ground acceleration time history of the 1940 EL Centro California earthquake ground motion [11]. The EL Centro earthquake time history in terms of ground acceleration and time is shown in Fig. 3. In the process of numerical integration for the time-history analysis the differential equation of motions are solved step-by-step, starting at zero time, when the displacement and velocity are presumably known. The time duration of earthquake of interest is divided into discrete intervals and analysis progress by successively ...
Context 2
... story drift ratios versus the number of building stories due to seismic loading by using amplified first-order analysis and second-order analysis, whereas variation of the stability coefficient and story drift versus the number of building stories due to real earthquake time-history analysis for symmetrical and asymmetrical buildings is show in Figs. 9 to ...
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This study concerns with the investigation of the second-order geometric nonlinearity effects of P-Delta analysis on the dynamic response of high rise steel buildings due to deterministic wind load. Linear and nonlinear time history analyses were conducted to analyze different tall steel building models adopted in the study. Five steel building mod...
Citations
... Increasing urbanisation has magnified seismic risk in seismic zones [1], and the resulting structural damage poses a major risk with significant social and economic impacts. Structural health monitoring (SHM) provides methods to detect, localise, and quantify damage after major events. ...
Structural health monitoring (SHM) methods provide damage metrics and localisation, but not a means of answering subsequent questions concerning immediate or long-term damage mitigation, risk, or safety in re-occupancy. Models based on the SHM results would provide a means to test these issues, but typically require extensive human input, which is not available immediately after an event to enhance and optimise immediate decision-making. This work presents a simple, readily automated modelling approach to translate SHM results from the proven hysteresis loop analysis (HLA) method into foundation models for immediate use. Experimental data from a 3-storey structure tested at the E-Defense facility in Japan are used to assess model performance. The model’s ability to capture the essential dynamics is assessed by comparing peak dynamic displacement and cross correlation coefficient (Rcoeff). For all 6 events, 3 storeys, and 2 directions, median (5-95% Range) of peak displacement error was 0.82 (0.17, 4.09) mm, and average Rcoeff = 0.82, all of which were significantly improved if the worst event was excluded. Overall, accurate nonlinear, time-varying baseline models were created using data from SHM damage identification and localisation methods using relatively quite simple model structures. The method is readily automated via algorithm, and the models were suitable for initial investigation and analysis on safety, damage mitigation, and thus re-occupancy. Such models could take SHM from being a tool for damage identification and extend it into further decision-making, creating far greater utility for engineers and owners, which could further spur impetus for investment in monitoring.
... Increasing urbanisation worldwide has resulted in increased housing development, concentration, and resulting greater number of taller buildings [1], in both non-seismic and seismic zones. Seismic damage is thus a major risk with significant follow-on social and economic impacts. ...
... Collapse prediction under seismic loading is one of the principal objectives of earthquake engineering [2][3][4]. In particular, large lateral displacements in tall structures can lead to P-delta effects [1,5]. P-delta effects increase overturning moments, generating effectively negative post-yield stiffnesses, and thus increase the likelihood of global collapse. ...
Prediction of building collapse due to significant seismic motion is a principle objective of earthquake engineers, particularly after a major seismic event when the structure is damaged and decisions may need to be made rapidly concerning the safe occupation of a building or surrounding areas. Traditional model-based pushover analyses are effective, but only if the structural properties are well understood, which is not the case after an event when that information is most useful. This paper combines hysteresis loop analysis (HLA) structural health monitoring (SHM) and incremental dynamic analysis (IDA) methods to identify and then analyse collapse capacity and the probability of collapse for a specific structure, at any time, a range of earthquake excitations to ensure robustness. This nonlinear dynamic analysis enables constant updating of building performance predictions following a given and subsequent earthquake events, which can result in difficult to identify deterioration of structural components and their resulting capacity, all of which is far more difficult using static pushover analysis. The combined methods and analysis provide near real-time updating of the collapse fragility curves as events progress, thus quantifying the change of collapse probability or seismic induced losses very soon after an earthquake for decision-making. Thus, this combination of methods enables a novel, higher-resolution analysis of risk that was not previously available. The methods are not computationally expensive and there is no requirement for a validated numerical model, thus providing a relatively simpler means of assessing collapse probability immediately post-event when such speed can provide better information for critical decision-making. Finally, the results also show a clear need to extend the area of SHM toward creating improved predictive models for analysis of subsequent events, where the Christchurch series of 2010–2011 had significant post-event aftershocks.
