Comparing Observed and Predicted Directivity in Near-Source Ground Motion

296

248

Ground motions with strong velocity pulses are of special concern for structural engineers. We describe an efficient and quantitative method to identify such ground motions. Previous algorithms to classify these pulse-like ground motions considered the ground motion in a single orientation, which made classifying pulses in arbitrary orientations difficult. We propose an algorithm that can identify pulses at arbitrary orientations in multicomponent ground motions, with little extra computational cost relative to a single-orientation calculation. We use continuous wavelet transforms of two orthogonal components of the ground motion to identify the orientations most likely to contain a pulse. The wavelet transform results are then used to extract pulses from the selected orientations, and a new classification criterion based on support vector machines is proposed. Because we are mostly interested in forward directivity pulses, which are found early in the time history, a criterion to reject pulses arriving late in the time history is also proposed. The procedure was used to classify ground motions in the Next Generation Attenuation-West2 database (Ancheta et al., 2013). The list of pulse-like ground motions was then manually filtered using sourceto-site geometry and site conditions to find the pulses most likely caused by directivity effects. Lists of both pulse-like ground motions and directivity ground motions are provided, along with the periods of the pulses and the orientations in which the pulses were strongest. Using the classification results, new models to predict the probability of a pulse and pulse period for a given future earthquake scenario are developed.

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm to Identify Strong-Velocity Pulses in Multicomponent Ground Motions

Shahi

Baker

2014

296

248

“…Pulselike ground motions are also observed in a range of orientations (e.g., Howard et al, 2005). To illustrate, Figure 2 shows the pulse indicator score as computed by the Baker (2007) algorithm at a site in different orientations (pulselike ground motions have high pulse indicator values).…”

Section: Identification Of Pulselike Ground Motionsmentioning

confidence: 99%

An Empirically Calibrated Framework for Including the Effects of Near-Fault Directivity in Probabilistic Seismic Hazard Analysis

Shahi

Baker

2011

261

275

Forward directivity effects are known to cause pulselike ground motions at near-fault sites. We propose a comprehensive framework to incorporate the effects of near-fault pulselike ground motions in probabilistic seismic hazard analysis (PSHA) computations. Also proposed is a new method to classify ground motions as pulselike or non-pulselike by rotating the ground motion and identifying pulses in all orientations. We have used this method to identify 179 recordings in the Next Generation Attenuation (NGA) database , where a pulselike ground motion is observed in at least one orientation. Information from these 179 recordings is used to fit several data-constrained models for predicting the probability of a pulselike ground motion occurring at a site, the orientations in which they are expected relative to the strike of the fault, the period of the pulselike feature, and the response spectrum amplification due to the presence of a pulselike feature in the ground motion. An algorithm describing how to use these new models in a modified PSHA computation is provided. The proposed framework is modular, which will allow for modification of one or more models as more knowledge is obtained in the future without changing other models or the overall framework. Finally, the new framework is compared with existing methods to account for similar effects in PSHA computation. Example applications are included to illustrate the use of the proposed framework, and implications for selection of ground motions for analysis of structures at near-fault sites are discussed.

“…Accordingly, their database consists of strong-motion records rotated to SN and SP directions, and the accuracy of their Sa SN prediction model depends on whether the maximum spectral value is aligned reliably with SN orientations. Howard et al (2005) evaluated the difference between the direction giving Sa MaxRot and the SN direction, and then the difference between spectral amplitudes of Sa MaxRot and the corresponding predictions of Sa SN at T ‫ס‬ 0.6, 0.75, 1.0, 1.5, 2.0, and 3.0 sec for each recording station. They found that for reverse-faulting records the misalignment of the direction of Sa MaxRot and the SN direction is up to 76Њ with an average difference of 29Њ.…”

Section: Sa Arbmentioning

confidence: 99%

Beyond SaGMRotI: Conversion to SaArb, SaSN, and SaMaxRot

Watson-Lamprey¹,

Boore²

2007

In the seismic design of structures, estimates of design forces are usually provided to the engineer in the form of elastic response spectra. Predictive equations for elastic response spectra are derived from empirical recordings of ground motion. The geometric mean of the two orthogonal horizontal components of motion is often used as the response value in these predictive equations, although it is not necessarily the most relevant estimate of forces within the structure. For some applications it is desirable to estimate the response value on a randomly chosen single component of ground motion, and in other applications the maximum response in a single direction is required. We give adjustment factors that allow converting the predictions of geometric-mean ground-motion predictions into either of these other two measures of seismic ground-motion intensity. In addition, we investigate the relation of the strike-normal component of ground motion to the maximum response values. We show that the strike-normal component of ground motion seldom corresponds to the maximum horizontal-component response value (in particular, at distances greater than about 3 km from faults), and that focusing on this case in exclusion of others can result in the underestimation of the maximum component. This research provides estimates of the maximum response value of a single component for all cases, not just near-fault strike-normal components. We provide modification factors that can be used to convert predictions of ground motions in terms of the geometric mean to the maximum spectral acceleration (Sa MaxRot ) and the random component of spectral acceleration (Sa Arb ). Included are modification factors for both the mean and the aleatory standard deviation of the logarithm of the motions.