We study ground-motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. In the first part of our study, we examine the observed response spectral (pseudo) accelerations for a selection of periods of engineering significance (1, 3, 6, and 8 s). Significant ground-motion amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. However, coherence is still observable at smaller length scales due to the high spatial density of the measurements. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. In the second part of the study, we test the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely three-dimensional (3D) finite-difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D community velocity models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations or match the details of the observed amplification patterns.
The populace of Los Angeles, California, was startled by shaking from the M 7.1 earthquake that struck the city of Ridgecrest located 200 km to the north on 6 July 2019. Although the earthquake did not cause damage in Los Angeles, the experience in high-rise buildings was frightening in contrast to the shaking felt in short buildings. Observations from 560 ground-level accelerometers reveal large variations in shaking in the Los Angeles basin that occurred for more than 2 min. The observations come from the spatially dense Community Seismic Network (CSN), combined with the sparser Southern California Seismic Network and California Strong Motion Instrumentation Program networks. Site amplification factors for periods of 1, 3, 6, and 8 s are computed as the ratio of each station’s response spectral values combined for the two horizontal directions, relative to the average of three bedrock sites. Spatially coherent behavior in site amplification emerges for periods ≥3 s, and the maximum calculated site amplifications are the largest, by factors of 7, 10, and 8, respectively, for 3, 6, and 8 s periods. The dense CSN observations show that the long-period amplification is clearly, but only partially, correlated with the depth to basement. Sites with the largest amplifications for the long periods (≥3 s) are not close to the deepest portion of the basin. At 6 and 8 s periods, the maximum amplifications occur in the western part of the Los Angeles basin and in the south-central San Fernando Valley sedimentary basin. The observations suggest that the excitation of a hypothetical high-rise located in an area characterized by the largest site amplifications could be four times larger than in a downtown Los Angeles location.
Sparse Bayesian learning (SBL) is a well-established technique for tackling supervised learning problems, while taking advantage of the prior knowledge that the expected solution is sparse. Based on the premise that initial damage of a structure appears only in a limited number of locations, SBL has been explored for identifying structural damage, showing promising results. Existing SBL methods for structural damage identification use measurements related to modal properties and are thus limited to linear models. In this paper, we present a methodology that allows for application of SBL in nonlinear models, using time history measurements. We develop a two-step optimization algorithm in which the most probable values of the structural model parameters and the hyperparameters are iteratively obtained. An equivalent, single-objective, minimization problem that results in the most probable model parameter values is also derived. We consider the example problem of identifying damage in the form of weld fractures in a 15-story moment-resisting steel-frame building, using a nonlinear finite-element model and simulated acceleration data. Fiber elements and a bilinear material model are used to account for the change in local stiffness when cracks at the welds are subjected to tension, and the model parameters characterize the loss of stiffness as the cracks open under tension. The damage identification results demonstrate the effectiveness and robustness of the proposed methodology in identifying the existence, location, and severity of damage for a variety of different damage scenarios and levels of model and measurement error.
Coherent patterns and large variations in ground shaking amplification were observed in the Los Angeles basin during the 2019 M7.1 Ridgecrest earthquake. In particular, 3 s to 6 s responses showed variations due to shallow basin geological structure that have implications for the response to large earthquakes of mid-rises, high-rises, long-span bridges, and fuel storage tanks, even if epicentral distances are several hundred kilometers. The Ridgecrest strong-motion data were recorded by seismic stations from the spatially dense Community Seismic Network, the Southern California Seismic Network, and the California Strong Motion Instrumentation Program. The mainshock observations are compared at the same locations with ground motion simulations to examine the regions that experienced the largest shaking, and to investigate the geological sources of large-amplitude shaking. The simulations were computed for the two most commonly-used regional community seismic velocity models, CVM-S4.26.M01 ('CVM-S') and CVM-H 15.1.0 ('CVM-H'). Both observations and simulations are used in dynamic analysis with a finite-element model of an existing high-rise with ~6-second fundamental horizontal periods, located in downtown Los Angeles. The geographical variation in maximum story drift, story-level shear force, and story-level moment values suggest that the excitation of a hypothetical high-rise located in an area characterized by the largest 6-s PSA values could be significantly larger than in a downtown Los Angeles location. Ground motion simulations using the CVM-H velocity model more closely predict the long-period site amplifications in greater Los Angeles, particularly in the south-central San Fernando Valley, than simulations using CVM-S.
There is an unprecedented increase in the number of real-time measurements produced by permanent, dense accelerometer arrays in buildings, an example being the Community Seismic Network. In the present work, damage identification techniques are developed by coupling such datasets with linear and nonlinear finite-element models of buildings. Damage in steel-frame buildings is manifested in localized areas as cracks in beam-column connections or as an average stiffness reduction. High-fidelity linear or nonlinear finite-element models are developed to allow for realistic behavior, including modeling nonlinearities associated with the opening and closing of cracks. L1 regularization techniques and sparse Bayesian learning tools are further developed fully in the time domain to reduce ill-conditioning and account for the sparsity of damage. The effectiveness of the proposed methods in identifying the location and severity of damage is demonstrated using simulated acceleration data from a three-story steel frame building, and a 15-story building in downtown Los Angeles that is fully instrumented.
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