On the Effect of Different Code-Based Ground Motion Selection Approaches for the Estimation of the Seismic Demand of Masonry Structures by Using Real Ground Motion Data Set

2021 ◽  
Author(s):  
Shaghayegh Karimzadeh ◽  
Sayed Mohammad Sajad Hussaini ◽  
‪Marco Francesco Funari ◽  
Paulo B. Lourenço
Keyword(s):  
Ground Motion ◽  
Seismic Demand ◽  
Masonry Structures ◽  
Data Set ◽  
Ground Motion Selection ◽  
Motion Data

scholarly journals A New Approach to Estimate a Mixed Model–Based Ground Motion Prediction Equation

2007 ◽  
Vol 23 (3) ◽  
pp. 665-684 ◽  
Author(s):  
Behrooz Tavakoli ◽  
Shahram Pezeshk
Keyword(s):  
Ground Motion ◽  
Mixed Model ◽  
Prediction Equation ◽  
Regression Coefficients ◽  
Motion Prediction ◽  
Ground Motion Prediction Equation ◽  
Predictive Equation ◽  
Ground Motion Prediction ◽  
Data Set ◽  
Motion Data

A derivative-free approach based on a hybrid genetic algorithm (HGA) is proposed to estimate a mixed model–based ground motion prediction equation (attenuation relationship) with several variance components. First, a simplex search algorithm (SSA) is used to reduce the search domain to improve the convergence speed. Then, a genetic algorithm (GA) is employed to obtain the regression coefficients and the uncertainties of a predictive equation in a unified framework using one-stage maximum-likelihood estimation. The proposed HGA results in a predictive equation that best fits a given ground motion data set. The proposed HGA is able to handle changes in the functional form of the equation. To demonstrate the solution quality of the proposed HGA, the regression coefficients and the uncertainties of a test function based on a simulated ground motion data set are obtained. Then, the proposed HGA is applied to fit two functional attenuation forms to an actual data set of ground motion. For illustration, the results of the HGA are compared with those used by previous conventional methods. The results indicate that the HGA is an appropriate algorithm to overcome the shortcomings of the previous methods and to provide reliable and stable solutions.

Consideration and Propagation of Ground Motion Selection Epistemic Uncertainties to Seismic Performance Metrics

2018 ◽  
Vol 34 (2) ◽  
pp. 587-610 ◽  
Author(s):  
Karim Tarbali ◽  
Brendon A. Bradley ◽  
Jack W. Baker
Keyword(s):  
Seismic Hazard ◽  
Seismic Response ◽  
Ground Motion ◽  
Seismic Performance ◽  
Performance Metrics ◽  
Epistemic Uncertainty ◽  
Seismic Demand ◽  
Intensity Measures ◽  
Ground Motion Selection ◽  
The Mean

This paper investigates various approaches to propagate the effect of epistemic uncertainty in seismic hazard and ground motion selection to seismic performance metrics. Specifically, three approaches with different levels of rigor are presented for establishing the conditional distribution of intensity measures considered for ground motion selection, selecting ground motion ensembles, and performing nonlinear response history analyses (RHAs) to probabilistically characterize seismic response. The mean and distribution of the seismic demand hazard is used as the principal means to compare the various results. An example application illustrates that, for seismic demand levels significantly below the collapse limit, epistemic uncertainty in seismic response resulting from ground motion selection can generally be considered as small relative to the uncertainty in the seismic hazard itself. In contrast, uncertainty resulting from ground motion selection appreciably increases the uncertainty in the seismic demand hazard for near-collapse demand levels.

scholarly journals Seismological and Engineering Characteristics of Strong Motion Data from 24 and 26 September 2019 Marmara Sea Earthquakes

2021 ◽  
Author(s):  
Fatma Sevil Malcıoğlu ◽  
Hakan Süleyman ◽  
Eser Çaktı
Keyword(s):  
Ground Motion ◽  
Prediction Models ◽  
Strong Motion ◽  
P Wave ◽  
Corner Frequency ◽  
Marmara Region ◽  
Marmara Sea ◽  
S Wave ◽  
Data Set ◽  
Motion Data

Abstract An MW 4.5 earthquake took place on September 24, 2019 in the Marmara Sea. Two days after, on September 26, 2019, Marmara region was rattled by an MW5.7 earthquake. With the intention of compiling an ample strong ground motion data set of recordings, we have utilized the stations of Istanbul Earthquake Rapid Response and Early Warning System operated by the Department of Earthquake Engineering of Boğaziçi University and of the National Strong Motion Network operated by AFAD. All together 438 individual records are used to calculate the source parameters of events; namely, corner frequency, radius, rupture area, average source dislocation, source duration and stress drop. Some of these parameters are compared with empirical relationships and discussed extensively. Duration characteristics are analyzed in two steps; first, by making use of the time difference between P-wave and S-wave onsets and then, by considering S-wave durations and significant durations. It is observed that they yield similar trends with global models. PGA, PGV and SA values are compared with three commonly used ground motion prediction models. At distances closer than about 60 km observed intensity measures mostly conform with the GMPE predictions. Beyond 60 km their attenuation is clearly faster than those of GMPEs. Frequency-dependent Q models are developed for both events. Their consistency with existing regional models are confirmed.

Ground Motion Selection in the Near-Fault Region considering Directivity-Induced Pulse Effects

2019 ◽  
Vol 35 (2) ◽  
pp. 759-786 ◽  
Author(s):  
Karim Tarbali ◽  
Brendon A. Bradley ◽  
Jack W. Baker
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
Seismic Hazard Analysis ◽  
Response Analysis ◽  
Seismic Demand ◽  
Ground Motion Selection ◽  
Near Fault ◽  
Fault Region

This paper focuses on the selection of ground motions for seismic response analysis in the near-fault region, where directivity effects are significant. An approach is presented to consider forward directivity velocity pulse effects in seismic hazard analysis without separate hazard calculations for ‘pulse-like’ and ‘non-pulse-like’ ground motions, resulting in a single target hazard (at the site of interest) for ground motion selection. The ability of ground motion selection methods to appropriately select records that exhibit pulse-like ground motions in the near-fault region is then examined. Applications for scenario and probabilistic seismic hazard analysis cases are examined through the computation of conditional seismic demand distributions and the seismic demand hazard. It is shown that ground motion selection based on an appropriate set of intensity measures (IMs) will lead to ground motion ensembles with an appropriate representation of the directivity-included target hazard in terms of IMs, which are themselves affected by directivity pulse effects. This alleviates the need to specify the proportion of pulse-like motions and their pulse periods a priori as strict criteria for ground motion selection.

A Neural Network for Automated Quality Screening of Ground Motion Records from Small Magnitude Earthquakes

2019 ◽  
Vol 35 (4) ◽  
pp. 1637-1661 ◽  
Author(s):  
Xavier Bellagamba ◽  
Robin Lee ◽  
Brendon A. Bradley
Keyword(s):  
Ground Motion ◽  
Low Cost ◽  
Feedforward Neural Networks ◽  
Earthquake Ground Motion ◽  
Data Sets ◽  
Data Set ◽  
Small Magnitude ◽  
Motion Data ◽  
Motion Models ◽  
Ground Motion Records

The ambitious scopes of recent earthquake ground motion studies are generating a need for more high-quality ground motion records. As the number of deployed sensors is rapidly growing through improved accessibility and cost (e.g., ground motion stations, low-cost accelerometers, smart phones), an exponentially increasing amount of data are being generated. Previously, quality-assured ground motion data sets for engineering applications were generated using both manual and automated quality screening methodologies. More recently, new techniques have emerged that potentially offer both improved classification accuracy and computational expediency. This work presents a machine learning–oriented method to facilitate and accelerate the quality classification of ground motion records from small magnitude earthquakes. Feedforward neural networks are selected for their ability to efficiently recognize patterns and are trained on two New Zealand data sets. An application to physics-based ground motion simulation validation indicates that the proposed approach delivers results that are comparable with manual quality selection. Robust automatic ground motion quality screening allows a significant increase in data set size for development, calibration, and validation of ground motion models.

Seismic Demand Sensitivity of Reinforced Concrete Structures to Ground Motion Selection and Modification Methods

2014 ◽  
Vol 30 (4) ◽  
pp. 1449-1465 ◽  
Author(s):  
Cristina Cantagallo ◽  
Guido Camata ◽  
Enrico Spacone
Keyword(s):  
Reinforced Concrete ◽  
Ground Motion ◽  
Demand Uncertainty ◽  
Concrete Structures ◽  
Nonlinear Dynamic Analysis ◽  
Sensitivity Study ◽  
Reinforced Concrete Structures ◽  
Seismic Demand ◽  
Ground Motion Selection ◽  
Structural Demand

The use of nonlinear dynamic analysis provides significant uncertainties on the seismic demand, especially when recorded ground motions are used. As these uncertainties strongly depend on ground motion selection and modification (GMSM) methods, a spectrum-compatibility criterion and a method based on the minimization of the scaling factor are compared in this work. The variability of a representative engineering demand parameter (EDP), obtained by subjecting ten reinforced concrete structures to different groups of records, is investigated through a sensitivity study based on the “Tornado diagram analysis.” The results show that the variability of the structural demand produced by the variation of the ground motion profile amplifies significantly with the increase in complexity and irregularity of the structures. More specifically, for regular structures, the selected GMSM criteria provide very similar variability while with the increase of irregularities, the spectrum-compatibility criterion produces a minimization of the demand uncertainty.

On Scaling of Earthquake Rise‐Time Estimates

2019 ◽  
Vol 109 (6) ◽  
pp. 2741-2745
Author(s):  
Alexander A. Gusev ◽  
Danila Chebrov
Keyword(s):  
Ground Motion ◽  
Strong Motion ◽  
Scaling Behavior ◽  
Corner Frequency ◽  
Source Spectrum ◽  
Self Similarity ◽  
Data Set ◽  
Motion Data ◽  
Time Estimates ◽  
Earthquake Ruptures

Abstract The scaling behavior of rise times Tr determined within earthquake source inversions that used strong‐motion data is determined using estimates as accumulated in the SRCMOD database. The Tr versus M0 trend derived from this data set is close to logTr=1/3logM0+ const; this agrees with the assumption of self‐similarity of earthquake ruptures. No biasing effect of station distance on Tr was found. The result was compared to recent scaling estimates based on mass teleseismic inversions. Absolute levels of teleseismic and local inversions match well; the slope of the trend of teleseismic estimates is somewhat more gradual. The absolute levels of Tr versus M0 trends recovered from finite source inversions may need reduction when used to predict parameters of near‐source ground motion. The observed scaling behavior of Tr is incompatible with the assumption that Tr defines the second corner frequency of the source spectrum.

Bayesian Estimations of Peak Ground Acceleration and 5% Damped Spectral Acceleration from Modified Mercalli Intensity Data

2003 ◽  
Vol 19 (3) ◽  
pp. 511-529 ◽  
Author(s):  
John E. Ebel ◽  
David J. Wald
Keyword(s):  
Ground Motion ◽  
Peak Ground Acceleration ◽  
Ground Motions ◽  
Spectral Acceleration ◽  
Intensity Data ◽  
Ground Acceleration ◽  
Data Set ◽  
Motion Data ◽  
Ground Motion Attenuation ◽  
Quantitative Estimates

We describe a new probabilistic method that uses observations of modified Mercalli intensity (MMI) from past earthquakes to make quantitative estimates of ground shaking parameters (i.e., peak ground acceleration, peak ground velocity, 5% damped spectral acceleration values, etc.). The method uses a Bayesian approach to make quantitative estimates of the probabilities of different levels of ground motions from intensity data given an earthquake of known location and magnitude. The method utilizes probability distributions from an intensity/ground motion data set along with a ground motion attenuation relation to estimate the ground motion from intensity. The ground motions with the highest probabilities are the ones most likely experienced at the site of the MMI observation. We test the method using MMI/ground motion data from California and published ground motion attenuation relations to estimate the ground motions for several earthquakes: 1999 Hector Mine, California (M7.1); 1988 Saguenay, Quebec (M5.9); and 1982 Gaza, New Hampshire (M4.4). In an example where the method is applied to a historic earthquake, we estimate that the peak ground accelerations associated with the 1727 (M∼5.2) earthquake at Newbury, Massachusetts, ranged from 0.23 g at Newbury to 0.06 g at Boston.

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