Seismic risk assessment considering cumulative damage due to aftershocks

Self Cite

208

194

Summary It is desirable that nonlinear dynamic analyses for structural fragility assessment are performed using unscaled ground motions. The widespread use of a simple dynamic analysis procedure known as Cloud Analysis, which uses unscaled records and linear regression, has been impeded by its alleged inaccuracies. This paper investigates fragility assessment based on Cloud Analysis by adopting, as the performance variable, a scalar demand to capacity ratio that is equal to unity at the onset of limit state. It is shown that the Cloud Analysis, performed based on a careful choice of records, leads to reasonable and efficient fragility estimates. There are 2 main rules to keep in mind for record selection: to make sure that a good portion of the records leads to a demand to capacity ratio greater than unity and that the dispersion in records' seismic intensity is considerable. An inevitable consequence of implementing these rules is that one often needs to deal with the so‐called collapse cases. To formally consider the collapse cases, a 5‐parameter fragility model is proposed that mixes the simple regression in the logarithmic scale with logistic regression. The joint distribution of fragility parameters can be obtained by adopting a Markov Chain Monte Carlo simulation scheme leading directly to the fragility and its confidence intervals. The resulting fragility curves compare reasonably with those obtained from the Incremental Dynamic Analysis and Multiple Stripe Analysis with (variable) conditional spectrum–compatible suites of records at different intensity levels for 3 older reinforced concrete frames with shear‐, shear‐flexure‐, and flexure‐dominant behavior.

P ((), C (|, S_{a})) = \frac{1}{1 + e^{- ((), α_{0} + α_{1} \cdot ln ((), S_{a}))}},

Section: Methodsmentioning

confidence: 99%

Analytical fragility assessment using unscaled ground motion records

Jalayer

Ebrahimian

Miano

et al. 2017

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208

194

“…This term is equal to unity in the cases of “collapse”; ie, in such cases, the LS (herein, damage , life‐safety , or near‐collapse ) is certainly exceeded. Finally, the probability of collapse P ( C | S a ) in Equation , which can be predicted by a logistic regression model (aka, logit) as a function of S a (see also), is expressed as follows:

P ((), C (|, S_{a})) = \frac{1}{1 + e^{- ((), α_{0} + α_{1} \cdot \ln ((), S_{a}))}},

where α o and α 1 are the parameters of the logistic regression. It is to note that the logistic regression model belongs to the family of generalized regression models and is particularly useful for cases in which the regression‐dependent variable is binary (ie, can have only two values 1 and 0, yes or no , which is the case of C and NoC herein).…”

Section: The Fragily Modelmentioning

confidence: 99%

Seismic reliability assessment and the nonergodicity in the modelling parameter uncertainties

Jalayer

Ebrahimian

2020

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Summary Modelling uncertainty can significantly affect the structural seismic reliability assessment. However, the limit state excursion due to this type of uncertainty may not be described by a Poisson process as it lacks renewal properties with the occurrence of each earthquake event. Furthermore, considering uncertainties related to ground motion representation by employing recorded ground motions together with modelling uncertainties is not a trivial task. Robust fragility assessment, proposed previously by the authors, employs the structural response to recorded ground motion as data in order to update prescribed seismic fragility models. Robust fragility can be extremely efficient for considering also the structural modelling uncertainties by creating a dataset of one‐to‐one assignments of structural model realizations and as‐recorded ground motions. This can reduce the computational effort by more than 1 order of magnitude. However, it should be kept in mind that the fragility concept itself is based on the underlying assumption of Poisson‐type renewal. Using the concept of updated robust reliability, considering both the uncertainty in ground motion representation based on as‐recorded ground motion and non ergodic modelling uncertainties, the error introduced through structural reliability assessment by using the robust fragility is quantified. It is shown through specific application to an existing RC frame that this error is quite small when the product of the time interval and the standard deviation of failure rate is small and is on the conservative side.

“…Focusing on damage accumulation in mainshock/aftershock contexts, Jalayer and Ebrahimian investigated cumulative damage in reinforced concrete (RC) buildings, considering the time‐dependent rate of aftershock occurrence. The study found significantly higher risks of damage when considering a structure damaged initially by a mainshock than a mainshock alone.…”

Section: Literature Reviewmentioning

confidence: 99%

Seismic loss and damage in light‐frame wood buildings from sequences of induced earthquakes

Chase

Liel

Luco

et al. 2019

Summary Activities related to oil and gas production, especially deep disposal of wastewater, have led to sequences of induced earthquakes in the central United States. This study aims to quantify damage to and seismic losses for light‐frame wood buildings when subjected to sequences of induced, small to moderate magnitude, events. To conduct this investigation, one‐ and two‐story multifamily wood frame buildings are designed, and their seismic response dynamically simulated using three‐dimensional nonlinear models, subjected to ground motion sequences recorded in induced events. Damage is quantified through seismic losses, which are estimated using the FEMA P‐58 methodology. Results show that at levels of shaking experienced in recent earthquakes, minor damage, consisting of cracking of interior finishes and nonstructural damage to plumbing and heating, ventilation, and air conditioning systems, is expected, which is consistent with observed damage in these events. The study also examines how expected losses and building fragility will accumulate and/or change over a sequence of earthquakes. Results indicate that damage quantified in terms of absorbed hysteretic energy tended to accumulate over the sequences; this damage corresponds to elongation or widening of cracks. However, fragility is not significantly altered by damage in a preceding event, meaning structures are not becoming more vulnerable due to existing damage. In addition, sequences of events do not change losses if the building is only repaired once at the end of the sequence, as the worsening of damage does not alter repair actions. If repairs are conducted after each event, though, total seismic losses can increase greatly from the sequence.