The G Summary Eqe Tools Plus
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SPO2IDA is introduced, a software tool that is capable of recreating the seismic behaviour of oscillators with complex quadrilinear backbones. It provides a direct connection between the static pushover (SPO) curve and the results of incremental dynamic analysis (IDA), a computer‐intensive procedure that offers thorough demand and capacity prediction capability by using a series of nonlinear dynamic analyses under a suitably scaled suite of ground motion records. To achieve this, the seismic behaviour of numerous single‐degree‐of‐freedom (SDOF) systems is investigated through IDA. The oscillators have a wide range of periods and feature pinching hysteresis with backbones ranging from simple bilinear to complex quadrilinear with an elastic, a hardening and a negative‐stiffness segment plus a final residual plateau that terminates with a drop to zero strength.
Jul 19, 2018 It is a very condensed summary of what you need to do to score high marks on DII, and includes a basic methodology with instructions on how to customize it yourself. Some analysis tools are also briefly explained. Current status? The G summary is up to date. The G summary is a summary of decisions/ opinions by the Enlarged Board of Appeal. It contains the headnotes as well as parts of the reasons. In the sidebar the summary provides keywords for easier access. For who is it? Although primarily developed for the EQE this tool may be of more general use for any.
An efficient method is introduced to treat the backbone shape by summarizing the analysis results into the 16, 50 and 84% fractile IDA curves, reducing them to a few shape parameters and finding simpler backbones that reproduce the IDA curves of complex ones. Thus, vast economies are realized while important intuition is gained on the role of the backbone shape to the seismic performance. The final product is SPO2IDA, an accurate, spreadsheet‐level tool for performance‐based earthquake engineering that can rapidly estimate demands and limit‐state capacities, strength reduction R‐factors and inelastic displacement ratios for any SDOF system with such a quadrilinear SPO curve. Copyright © 2006 John Wiley & Sons, Ltd. Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA) Liverpool, UK July 13-16, 2014 Vulnerability, Uncertainty, and Risk Proceedings American Society of Civil Engineers Reston, VA, (2014)., (2014).
6/609, 10.10400239 Tiziana Rossetto, Pierre Gehl, Stylianos Minas, Arash Nassirpour, Joshua Macabuag, Philippe Duffour and John Douglas Sensitivity Analysis of Different Capacity Spectrum Approaches to Assumptions in the Modeling, Capacity and Demand Representations, (2014)., (2014). 1665 16/609.167, 10.10413609.139 • Mohammadjavad Hamidia, Andre Filiatrault and Amjad Aref, Simplified seismic sidesway collapse analysis of frame buildings, Earthquake Engineering & Structural Dynamics, 43, 3, (429), (2014).
A predictive stochastic model is developed based on regression relations that inputs a given earthquake scenario description and outputs seismic ground acceleration time histories at a site of interest. A bimodal parametric non‐stationary Kanai‐Tajimi (K‐T) ground motion model lies at the core of the proposed predictive model.
The functional forms that describe the temporal evolution of the K‐T model parameters can effectively represent strong non‐stationarities of the ground motion. Fully non‐stationary ground motion time histories can be generated through the powerful Spectral Representation Method. A Californian subset of the available NGA‐West2 database is used to develop and calibrate the predictive model. Samples of the model parameters are obtained by fitting the K‐T model to the database records, and the resulting marginal distributions of the model parameters are efficiently described by standard probability models. The samples are translated to the standard normal space and linear random‐effect regression models are established relating the transformed normal parameters to the commonly used earthquake scenario defining predictors: moment magnitude M w, closest‐to‐site distance R r u p, and average shear‐wave velocity V S 30 at a site of interest. The random‐effect terms in the developed regression models can effectively model the correlation among ground motions of the same earthquake event, in parallel to taking into account the location‐dependent effects of each site. For validation purposes, simulated acceleration time histories based on the proposed predictive model are compared with recorded ground motions.