Hope for the Best, Prepare for the Worst: Response of Tall Steel Buildings to the ShakeOut Scenario Earthquake

This work represents an effort to develop one plausible realization of the effects of the scenario event on tall steel moment-frame buildings. We have used the simulated ground motions with three-dimensional nonlinear finite element models of three buildings in the 20-story class to simulate structural responses at 784 analysis sites spaced at approximately 4 km throughout the San Fernando Valley, the San Gabriel Valley, and the Los Angeles Basin. Based on the simulation results and available information on the number and distribution of steel buildings, the recommended damage scenario for the ShakeOut drill was 5% of the estimated 150 steel moment-frame structures in the 10–30 story range collapsing, 10% red-tagged, 15% with damage serious enough to cause loss of life, and 20% with visible damage requiring building closure

Application of Stochastic Simulation Methods to System Identification

Reliable predictive models for the response of structures are a necessity for many branches of earthquake engineering, such as design, structural control, and structural health monitoring. However, the process of choosing an appropriate class of models to describe a system, known as model-class selection, and identifying the specific predictive model based on available data, known as system identification, is difficult. Variability in material properties, complex constitutive behavior, uncertainty in the excitations caused by earthquakes, and limited constraining information (relatively few channels of data, compared to the number of parameters needed for a useful predictive model) make system identification an ill-conditioned problem. In addition, model-class selection is not trivial, as it involves balancing predictive power with simplicity. These problems of system identification and model-class selection may be addressed using a Bayesian probabilistic framework that provides a rational, transparent method for combining prior knowledge of a system with measured data and for choosing between competing model classes. The probabilistic framework also allows for explicit quantification of the uncertainties associated with modeling a system. The essential idea is to use probability logic and Bayes' Theorem to give a measure of plausibility for a model or class of models that is updated with available data. Similar approaches have been used in the field of system identification, but many currently used methods for Bayesian updating focus on the model defined by the set of most plausible parameter values. The challenge for these approaches (referred to as asymptotic-approximation-based methods) is when one must deal with ill-conditioned problems, where there may be many models with high plausibility, rather than a single v dominant model. It is demonstrated here that ill-conditioned problems in system identification and model-class selection can be effectively addressed using stochastic simulation methods. This work focuses on the application of stochastic simulation to updating and comparing model classes in problems of: (1) development of empirical ground motion attenuation relations, (2) structural model updating using incomplete modal data for the purposes of structural health monitoring, and (3) identification of hysteretic structural models, including degrading models, from seismic structural response. The results for system identification and model-class selection in this work fall into three categories. First, in cases where the existing asymptotic approximation-based methods are appropriate (i.e., well-conditioned problems with one highest-plausibility model), the results obtained using stochastic simulation show good agreement with results from asymptotic-approximation-based methods. Second, for cases involving ill-conditioned problems based on simulated data, stochastic simulation methods are successfully applied to obtain results in a situation where the use of asymptotics is not feasible (specfically, the identification of hysteretic models). Third, preliminary studies using stochastic simulation to identify a deteriorating hysteretic model with relatively sparse real data from a structure damaged in the 1994 Northridge earthquake show that the high-plausibility models demonstrate behavior consistent with the observed damage, indicating that there is promise in applying these methods to ill-conditioned problems in the real world

Bayesian Updating and Model Class Selection of Deteriorating Hysteretic Structural Models using Seismic Response Data

Identification of structural models from measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. System identification using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures where the restoring forces depend on the previous time history of the structural response rather than on an instantaneous finite-dimensional state. Furthermore, this inverse problem is ill-conditioned because even if some components in the structure show substantial yielding, others will exhibit nearly elastic response, producing no information about their yielding behavior. Classical least-squares or maximum likelihood estimation will not work with a realistic class of hysteretic models because it will be unidentifiable based on the data. On the other hand, Bayesian updating and model class selection provide a powerful and rigorous approach to tackle this problem when implemented using Markov Chain Monte Carlo simulation methods such as the Metropolis-Hastings, Gibbs Sampler and Hybrid Monte Carlo algorithms. The emergence of these stochastic simulation methods in recent years has led to a renaissance in Bayesian methods across all disciplines in science and engineering because the high-dimensional integrations that are involved can now be readily evaluated. The power of these methods to handle ill-conditioned or unidentifiable system identification problems is demonstrated by using a recently-developed stochastic simulation algorithm, Transitional Markov Chain Monte Carlo, to perform Bayesian updating and model class selection on a class of Masing hysteretic structural models that are relatively simple yet can give realistic responses to seismic loading. Examples will be given using deteriorating hysteretic building models with simulated seismic response data

SHAKEOUT 2008: Tall Steel Moment Frame Building Response

In 2008, there was a significant campaign undertaken in southern California to increase public awareness and readiness for the next large earthquake along the San Andreas fault that culminated in a large-scale earthquake response exercise. The USGS ShakeOut scenario was a key element to understanding the likely effects of such an event. In support of this effort, a study was conducted to assess the response of tall steel structures to a M7.8 scenario earthquake on the southern San Andreas Fault. Presented here are results for two structures. The first is a model of an 18-story steel moment frame building that experienced significant damage (fracture of moment-frame connections) during the 1994 Northridge earthquake. The second model is of a very similar building, but with a structural system redesigned according to a more modern code (UBC 97). Structural responses are generated using three-dimensional, non-linear, deteriorating finite element models, which are subjected to ground motions generated by the scenario earthquake at 784 points spaced at approximately 4 km throughout the San Fernando Valley, the San Gabriel Valley and the Los Angeles Basin. The kinematic source model includes large-scale features of the slip distribution, determined through community participation in two workshops and short lengthscale random variations. The rupture initiates at Bombay Beach and ruptures to the northwest before ending at Lake Hughes, with a total length of just over 300 km and a peak slip of 12 m at depth. The resulting seismic waves are propagated using the SCEC community velocity model for southern California, resulting in ground velocities as large as 2 m/s and ground displacements as large as 1.5 m in the region considered in this study. The ground motions at the sites selected for this study are low-passed filtered with a corner period at 2 seconds. Results indicate a high probability of collapse or damage for the pre-1994 building in areas of southern California where many high-rise buildings are located. Performance of the redesigned buildings is substantially improved, but responses in urban areas are still large enough to indicate a high-probability of damage. The simulation results are also used to correlate the probability of building collapse with damage to the structural system

Rapid Estimation of Damage to Tall Buildings Using Near Real‐Time Earthquake and Archived Structural Simulations

This article outlines a new approach to rapidly estimate the damage to tall buildings immediately following a large earthquake. The preevent groundwork involves the creation of a database of structural responses to a suite of idealized ground‐motion waveforms. The postevent action involves (1) rapid generation of an earthquake source model, (2) near real‐time simulation of the earthquake using a regional spectral‐element model of the earth and computing synthetic seismograms at tall building sites, and (3) estimation of tall building response (and damage) by determining the best‐fitting idealized waveforms to the synthetically generated ground motion at the site and directly extracting structural response metrics from the database. Here, ground‐velocity waveforms are parameterized using sawtoothlike wave trains with a characteristic period (T), amplitude (peak ground velocity, PGV), and duration (number of cycles, N). The proof‐of‐concept is established using the case study of one tall building model. Nonlinear analyses are performed on the model subjected to the idealized wave trains, with T varying from 0.5 s to 6.0 s, PGV varying from 0.125  m/s, and N varying from 1 to 5. Databases of peak transient and residual interstory drift ratios (IDR), and permanent roof drift are created. We demonstrate the effectiveness of the rapid response approach by applying it to synthetic waveforms from a simulated 1857‐like magnitude 7.9 San Andreas earthquake. The peak IDR, a key measure of structural performance, is predicted well enough for emergency response decision making

Forward Genetic Analysis of Visual Behavior in Zebrafish

The visual system converts the distribution and wavelengths of photons entering the eye into patterns of neuronal activity, which then drive motor and endocrine behavioral responses. The gene products important for visual processing by a living and behaving vertebrate animal have not been identified in an unbiased fashion. Likewise, the genes that affect development of the nervous system to shape visual function later in life are largely unknown. Here we have set out to close this gap in our understanding by using a forward genetic approach in zebrafish. Moving stimuli evoke two innate reflexes in zebrafish larvae, the optomotor and the optokinetic response, providing two rapid and quantitative tests to assess visual function in wild-type (WT) and mutant animals. These behavioral assays were used in a high-throughput screen, encompassing over half a million fish. In almost 2,000 F2 families mutagenized with ethylnitrosourea, we discovered 53 recessive mutations in 41 genes. These new mutations have generated a broad spectrum of phenotypes, which vary in specificity and severity, but can be placed into only a handful of classes. Developmental phenotypes include complete absence or abnormal morphogenesis of photoreceptors, and deficits in ganglion cell differentiation or axon targeting. Other mutations evidently leave neuronal circuits intact, but disrupt phototransduction, light adaptation, or behavior-specific responses. Almost all of the mutants are morphologically indistinguishable from WT, and many survive to adulthood. Genetic linkage mapping and initial molecular analyses show that our approach was effective in identifying genes with functions specific to the visual system. This collection of zebrafish behavioral mutants provides a novel resource for the study of normal vision and its genetic disorders

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin