Vine Copula-Based Dependence Modeling of Multivariate Ground-Motion Intensity Measures and the Impact on Probabilistic Seismic Slope Displacement Hazard Analysis

2020 ◽  
Vol 110 (6) ◽  
pp. 2967-2990 ◽  
Author(s):  
Mao-Xin Wang ◽  
Duruo Huang ◽  
Gang Wang ◽  
Wenqi Du ◽  
Dian-Qing Li
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Multivariate Normality ◽  
Engineering Practice ◽  
Intensity Measures ◽  
Normality Assumption ◽  
Vine Copula ◽  
The Impact ◽  
Slope Displacement ◽  
Dependence Structures

ABSTRACT Multivariate normality of logarithmic intensity measures (IMs) is conventionally assumed in earthquake engineering applications. This article introduces a vine copula approach as a useful tool for multivariate modeling of IMs. This approach provides a flexible way to decompose a joint distribution into individual marginal distributions and multiple dependences characterized by a cascade of bivariate copulas (pair-copulas), whereas the conventional multivariate normality can be considered as a special case of the vine copula model. Based on the Next Generation Attenuation-West1 database and various combinations of ground-motion prediction equations (GMPEs), the optimal dependence structures among peak ground acceleration, peak ground velocity, and Arias intensity, as well as that for spectral accelerations at four periods, are identified. The joint normality assumption for the two vector sets of logarithmic IMs is examined from the perspective of copula theory. The results illustrate that the normality assumption is generally adequate for bivariate IMs but may not be optimal for multivariate IMs. Using the same set of GMPEs (developed by the same researchers) may improve the joint normality for logarithmic IMs. Furthermore, the impact of dependence structures among IMs on probabilistic seismic slope displacement hazard analysis is explored. The results indicate that using the same Pearson correlation coefficients but different dependence structures for IMs produces different hazard results and this difference is generally enlarged with increasing hazard levels. As hazard difference from different dependence structures is generally not significant, the multivariate normality distribution for logarithmic IMs is judged to be an acceptable assumption in engineering practice. Alternatively, engineers may make a choice between the joint normal distribution and the vine copula tool depending on the specific situation because of the better generality of the latter.

Prediction of Ground-Motion Parameters for the NGA-West2 Database Using Refined Second-Order Deep Neural Networks

2021 ◽  
Author(s):  
Duofa Ji ◽  
Chenxi Li ◽  
Changhai Zhai ◽  
You Dong ◽  
Evangelos I. Katsanos ◽  
...  
Keyword(s):  
Ground Motion ◽  
Earthquake Engineering ◽  
Hazard Analysis ◽  
A Priori ◽  
Absolute Error ◽  
Second Order ◽  
Prediction Errors ◽  
Intensity Measures ◽  
Engineering Research ◽  
Motion Models

ABSTRACT One of the key elements within seismic hazard analysis is the establishment of appropriate ground-motion models (GMMs), which are used to predict the levels of ground-motion intensities by considering various parameters (e.g., source, path, and site). Many empirical GMMs were derived on the basis of a predefined linear or nonlinear equation that is heavily dependent on the a priori knowledge of a functional form that varies between the modelers’ choices. To overcome this issue, this study develops a deep neural network (DNN) trained by the recordings from the Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation-West2 Project (NGA-West2) database. To this end, we collected 20,900 ground motion recordings from the database and randomly split them into the training, validation, and testing datasets. The refined second-order neuron is proposed to solve the problem, and the Adam optimizer is used to optimize the performance of the model. The prediction errors are evaluated by three performance indicators (i.e., R2, root mean square error, mean absolute error), and the predictive results are compared with previous GMMs developed based on the PEER NGA-West2 database. The between-event and within-event standard deviations (SDs) as well as total SDs are calculated and compared. Based on the comparisons, our model maintains consistent performance (e.g., the dependence of predicted intensity measures on seismological and site-specific parameters) with the compared GMM. Its relatively small total SDs, especially for longer periods, confirm that the proposed model is associated with better predictive power.

scholarly journals The effect of spatial dependence on hazard validation

2017 ◽  
Vol 209 (3) ◽  
pp. 1363-1368 ◽  
Author(s):  
Iunio Iervolino ◽  
Massimiliano Giorgio ◽  
Pasquale Cito
Keyword(s):  
Ground Motion ◽  
Spatial Dependence ◽  
Best Practice ◽  
Hazard Analysis ◽  
Time Span ◽  
Probabilistic Seismic Hazard ◽  
Monitoring Networks ◽  
Hazard Maps ◽  
Intensity Measures ◽  
Seismic Networks

Summary In countries where best-practice probabilistic hazard studies and seismic monitoring networks are available, there is increasing interest in direct validation of hazard maps. It usually means trying to quantitatively understand whether probabilities estimated via hazard analysis are consistent with observed frequencies of exceedance of ground motion intensity thresholds. Because the exceedance events of interest are typically rare with respect to the time span covered by data from seismic networks, a common approach underlying these studies is to pool observations from different sites. The main reason for this is to collect a sample large enough to convincingly perform a statistical analysis. However, this requires accounting for the dependence among the stochastic processes counting exceedances of ground motion intensity measures thresholds at different sites. Neglecting this dependence may lead to potentially fallacious conclusions about inadequateness of probabilistic seismic hazard. This study addresses this issue revisiting a hazard validation exercise for Italy, showing that accounting for this kind of spatial dependence can change the results of formal testing.

Vector and Scalar IMs in Structural Response Estimation, Part II: Building Demand Assessment

2016 ◽  
Vol 32 (3) ◽  
pp. 1525-1543 ◽  
Author(s):  
Mohsen Kohrangi ◽  
Paolo Bazzurro ◽  
Dimitrios Vamvatsikos
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Three Dimensional ◽  
Structural Response ◽  
Response Assessment ◽  
Companion Paper ◽  
Period Range ◽  
Intensity Measures ◽  
Advantages And Disadvantages ◽  
Story Drift

The advantages and disadvantages of using scalar and vector ground motion intensity measures (IMs) are discussed for the local, story-level seismic response assessment of three-dimensional (3-D) buildings. Candidate IMs are spectral accelerations, at a single period ( Sa) or averaged over a period range ( Sa avg). Consistent scalar and vector probabilistic seismic hazard analysis results were derived for each IM, as described in the companion paper in this issue ( Kohrangi et al. 2016 ). The response hazard curves were computed for three buildings with reinforced concrete infilled frames using the different IMs as predictors. Among the scalar IMs, Sa avg tends to be the best predictor of both floor accelerations and inter story drift ratios at practically any floor. However, there is an improvement in response estimation efficiency when employing vector IMs, specifically for 3-D buildings subjected to both horizontal components of ground motion. This improvement is shown to be most significant for a tall plan-asymmetric building.

Hazard characterization for alternative intensity measures using the total probability theorem

2021 ◽  
pp. 875529302110492
Author(s):  
Michael W Greenfield ◽  
Andrew J Makdisi
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Total Probability ◽  
Ground Acceleration ◽  
Intensity Measures ◽  
Hazard Curve ◽  
Cumulative Absolute Velocity ◽  
Liquefaction Hazard ◽  
Hazard Curves ◽  
Total Probability Theorem

Since their inception in the 1980s, simplified procedures for the analysis of liquefaction hazards have typically characterized seismic loading using a combination of peak ground acceleration and earthquake magnitude. However, more recent studies suggest that certain evolutionary intensity measures (IMs) such as Arias intensity or cumulative absolute velocity may be more efficient and sufficient predictors of liquefaction triggering and its consequences. Despite this advantage, widespread hazard characterizations for evolutionary IMs are not yet feasible due to a relatively incomplete representation of the ground motion models (GMMs) needed for probabilistic seismic hazard analysis (PSHA). Without widely available hazard curves for evolutionary IMs, current design codes often rely on spectral targets for ground motion selection and scaling, which are shown in this study to indirectly result in low precision of evolutionary IMs often associated with liquefaction hazards. This study presents a method to calculate hazard curves for arbitrary intensity measures, such as evolutionary IMs for liquefaction hazard analyses, without requiring an existing GMM. The method involves the conversion of a known IM hazard curve into an alternative IM hazard curve using the total probability theorem. The effectiveness of the method is illustrated by comparing hazard curves calculated using the total probability theorem to the results of a PSHA to demonstrate that the proposed method does not result in additional uncertainty under idealized conditions and provides a range of possible hazard values under most practical conditions. The total probability theorem method can be utilized by practitioners and researchers to select ground motion time series that target alternative IMs for liquefaction hazard analyses or other geotechnical applications. This method also allows researchers to investigate the efficiency, sufficiency, and predictability of new, alternative IMs without necessarily requiring GMMs.

Efficiency of ground motion intensity measures with earthquake-induced earth dam deformations

2020 ◽  
pp. 875529302093881
Author(s):  
Richard Armstrong ◽  
Tadahiro Kishida ◽  
DongSoon Park
Keyword(s):  
Ground Motion ◽  
Hazard Analysis ◽  
Earth Dam ◽  
Deformation Analysis ◽  
Embankment Dam ◽  
Natural Period ◽  
Intensity Measures ◽  
Embankment Dams ◽  
Engineering Demand Parameter ◽  
Almost All

In a seismic hazard analysis (SHA), the earthquake loading level should be predicted for one or more ground motion intensity measures (IMs) that are expected to relate well with the engineering demand parameters (EDPs) of the site. In this study, the goal was to determine the IMs that best relate to embankment dam deformations based on nonlinear deformation analysis (NDA) results of two embankment dams with a large suite of recorded ground motions. The measure utilized to determine the “best” IM was standard deviation in the engineering demand parameter (e.g., deformation) for a given IM, also termed “efficiency.” Results of the study demonstrated that for the NDA model used, Arias intensity (AI) was found to be the most efficient predictor of embankment dam deformations. In terms of pseudo-spectral acceleration (PSA)-based IMs, the PSA at short periods and then in the general range of the natural period of the dams was seen to be the most efficient IM, but was in almost all cases not as efficient as AI.

scholarly journals Probabilistic seismic hazard analysis using a new ground motion intensity measure

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 206-215 ◽  
Author(s):  
Robespiere Chávez López ◽  
Edén Bojórquez-Mora
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Probabilistic Seismic Hazard Analysis ◽  
Seismic Hazard Analysis ◽  
Probabilistic Seismic Hazard ◽  
Intensity Measure ◽  
Intensity Measures ◽  
First Time ◽  
Ground Motion Intensity Measure

The main objective of this work is to compute the probabilistic seismic hazard analysis for a region of Mexico using a new ground motion intensity measure which is based on the spectral acceleration and a parameter proxy of the spectral shape named Np. The motivation of using this new ground motion intensity measure is because recently it has demonstrated its potential in predicting the response of buildings subjected to earthquakes. In fact, it was demonstrated that intensity measures based on Np are more efficient compared with other parameter of the literature. It is important to mention that this is the first time that a probabilistic seismic hazard analysis is performed using this new intensity measurement.

Applied Incremental Dynamic Analysis

2004 ◽  
Vol 20 (2) ◽  
pp. 523-553 ◽  
Author(s):  
Dimitrios Vamvatsikos ◽  
C. Allin Cornell
Keyword(s):  
Dynamic Analysis ◽  
Ground Motion ◽  
Earthquake Engineering ◽  
Hazard Analysis ◽  
Limit State ◽  
Incremental Dynamic Analysis ◽  
Test Bed ◽  
Limit States ◽  
Intensity Measures ◽  
Structural Demand

We are presenting a practical and detailed example of how to perform incremental dynamic analysis (IDA), interpret the results and apply them to performance-based earthquake engineering. IDA is an emerging analysis method that offers thorough seismic demand and capacity prediction capability by using a series of nonlinear dynamic analyses under a multiply scaled suite of ground motion records. Realization of its opportunities requires several steps and the use of innovative techniques at each one of them. Using a nine-story steel moment-resisting frame with fracturing connections as a test bed, the reader is guided through each step of IDA: (1) choosing suitable ground motion intensity measures and representative damage measures, (2) using appropriate algorithms to select the record scaling, (3) employing proper interpolation and (4) summarization techniques for multiple records to estimate the probability distribution of the structural demand given the seismic intensity, and (5) defining limit-states, such as the dynamic global system instability, to calculate the corresponding capacities. Finally, (6) the results can be used to gain intuition for the structural behavior, highlighting the connection between the static pushover (SPO) and the dynamic response, or (7) they can be integrated with conventional probabilistic seismic hazard analysis (PSHA) to estimate mean annual frequencies of limit-state exceedance. Building upon this detailed example based on the nine-story structure, a complete commentary is provided, discussing the choices that are available to the user, and showing their implications for each step of the IDA.

scholarly journals Vector-Valued Fragility Analysis Using PGA and PGV Simultaneously as Ground-Motion Intensity Measures

2010 ◽  
Vol 5 (4) ◽  
pp. 407-416
Author(s):  
Sei’ichiro Fukushima ◽  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Fragility Curves ◽  
Fragility Analysis ◽  
Peak Ground Velocity ◽  
Seismic Risk Analysis ◽  
Ground Acceleration ◽  
Intensity Measures ◽  
Vector Valued

Seismic risk analysis usually expresses ground-motion intensity using a single index such as peak ground acceleration (PGA), spectral acceleration for a specified period, or peak ground velocity (PGV). Limiting the number of indices, however, adds greater uncertainty when estimating annual failure probability given by convolving seismic hazard and fragility curves. This is because information other than ground-motion intensity is missing. Author proposed seismic hazard analysis using PGA and PGV simultaneously as groundmotion input measures. After analyzing the correlation coefficient between PGA and PGV using K-NET and KiK-net databases, probabilistic seismic hazard for seven sites in Kanto district in Japan was evaluated. In this study, seismic fragility analysis using PGA and PGV is conducted followed by advantage of vector-valued fragility analysis.

Food Safety Security: A new Concept for Enhancing Food Safety Measures

2012 ◽  
Vol 82 (3) ◽  
pp. 216-222 ◽  
Author(s):  
Venkatesh Iyengar ◽  
Ibrahim Elmadfa
Keyword(s):  
Food Safety ◽  
Hazard Analysis ◽  
Warning System ◽  
Shared Responsibility ◽  
Fine Tuning ◽  
Strategic Action ◽  
Surveillance Network ◽  
Measurement Systems ◽  
Critical Control Points ◽  
The Impact

The food safety security (FSS) concept is perceived as an early warning system for minimizing food safety (FS) breaches, and it functions in conjunction with existing FS measures. Essentially, the function of FS and FSS measures can be visualized in two parts: (i) the FS preventive measures as actions taken at the stem level, and (ii) the FSS interventions as actions taken at the root level, to enhance the impact of the implemented safety steps. In practice, along with FS, FSS also draws its support from (i) legislative directives and regulatory measures for enforcing verifiable, timely, and effective compliance; (ii) measurement systems in place for sustained quality assurance; and (iii) shared responsibility to ensure cohesion among all the stakeholders namely, policy makers, regulators, food producers, processors and distributors, and consumers. However, the functional framework of FSS differs from that of FS by way of: (i) retooling the vulnerable segments of the preventive features of existing FS measures; (ii) fine-tuning response systems to efficiently preempt the FS breaches; (iii) building a long-term nutrient and toxicant surveillance network based on validated measurement systems functioning in real time; (iv) focusing on crisp, clear, and correct communication that resonates among all the stakeholders; and (v) developing inter-disciplinary human resources to meet ever-increasing FS challenges. Important determinants of FSS include: (i) strengthening international dialogue for refining regulatory reforms and addressing emerging risks; (ii) developing innovative and strategic action points for intervention {in addition to Hazard Analysis and Critical Control Points (HACCP) procedures]; and (iii) introducing additional science-based tools such as metrology-based measurement systems.

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