Regional Seismic Risk Assessment Research Articles

Structure-to-structure damage correlation for scenario-based regional seismic risk assessment

When performing a regional seismic risk assessment, one is interested in estimating the damage, losses, and, in general, the seismic performance of several structures spatially distributed within a region. In this type of probabilistic risk analysis, it is common to consider the spatial correlation between the ground motion intensities affecting different locations. However, the correlation of damage experienced by different structures conditioned on their levels of intensities, herein referred to as structure-to-structure damage correlation, is usually neglected. This study evaluates the effect of the structure-to-structure damage correlation and proposes a simplified model for incorporating it into regional seismic risk assessment through the use of copula functions. In order to illustrate the influence of the structure-to-structure damage correlation on the regional seismic risk, a case study in San Carlos, California, is analyzed considering and neglecting the structure-to-structure damage correlation. Results show that neglecting the structure-to-structure damage correlation may lead to significant underestimations of large-consequence risks.

Evaluation of intensity measure performance in regional seismic risk assessment of reinforced concrete bridge inventories

Seismic risk assessment requires fragility functions derived using non-linear time-history analysis with earthquake records compatible with the site hazard and characterised via a suitable intensity measure (IM), which poses a challenge given the wide variety of structural characteristics found in bridge inventories. Given the lack of consensus on a suitable IM, regional studies often use peak ground acceleration (PGA), which despite being recognized as a poor indicator of structural performance, has persisted in earthquake response characterisation. Average spectral acceleration (AvgSa) has recently gained popularity since it describes earthquake intensity over a range of pertinent periods of vibration; however, its suitability as an IM has not been demonstrated on real bridge inventories with a wide variety of structural characteristics. In this study, hazard-consistent records selected for both PGA and AvgSa are used to evaluate the response of a large inventory of existing bridges with varying structural characteristics, from the Italian roadway network. The results indicate that AvgSa is a more accurate IM compared to PGA when applied to the regional assessment of RC bridges.

Multihazard Scenarios for Regional Seismic Risk Assessment of Spatially Distributed Infrastructure

AbstractThis paper introduces the multihazard optimization-based probabilistic scenario (multihazard OPS) method to create an ensemble of multihazard scenarios that can be used for efficient evalua...

Intensity Measures for Regional Seismic Risk Assessment of Low-Rise Wood-Frame Residential Construction

AbstractGround motion intensity measures (IMs) provide a link between the probabilistic seismic hazard analysis and probabilistic structural response analysis. IMs that are well correlated with the...

Regional Seismic Risk Assessment of Infrastructure Systems through Machine Learning: Active Learning Approach

Regional seismic risk assessment involves many infrastructure systems, and it is computationally intensive to conduct an individual simulation of each system. This paper suggests an approach using active learning to select informative samples that help build machine learning models with fewer samples for regional damage assessment. The potential of the approach is demonstrated with (1) failure mode prediction of bridge columns, and (2) regional damage assessment of the California two-span bridge inventory with seat abutments. The active learning approach involves the selection of column attributes or bridge models that are more informative to the creation of machine learning-based decision boundaries. The results reveal that an active learning target model based on 100 bridge samples can achieve a level of accuracy of 80%, which is equivalent to a machine learning model based on 480 bridge samples in the case of damage prediction following an earthquake. With the proposed approach, the computational complexity associated with regional risk assessment of bridge systems with specific attributes can be drastically reduced. The proposed approach also will help plan experimental studies that are more informative for damage assessment.

Toward confident regional seismic risk assessment of spatially distributed structural portfolios via entropy-based intensity measure selection

Intensity measure (IM) selection is a crucial step in regional seismic risk assessment (RSRA) of spatially distributed structural portfolios. In order to facilitate more confident regional seismic risk estimates, this study proposes an entropy-based IM selection methodology, offering the first systematic and quantitative regional-level IM selection approach. By conceptualizing the spatially distributed structural portfolio as an integrated multi-response structural system, the joint entropy of the system’s unconditional seismic demands is leveraged as an IM evaluation criterion. Owing to the adaptation of a newly developed advanced IM co-simulation method and multivariate surrogate demand modeling techniques, this entropy-based IM selection approach is able to holistically incorporate uncertainties rising from the spatial IM random field, structural parameters, and surrogate demand models, during the course of uncertainty propagation in RSRA. The efficacy of the proposed methodology is demonstrated along with practical heuristics for alleviating the computational burden, based on a hypothetical highway bridge portfolio. Different application cases in the context of RSRA are considered, including pre-event RSRA considering a single scenario-earthquake as well as a stochastic earthquake catalog, and post-event RSRA considering record updating. The results consistently highlight the significance of the proposed IM selection method in facilitating more confident regional seismic risk estimates. Moreover, this study also provides valuable insights into record updating in reducing the level of uncertainty of the spatial IM random field, and its implication on IM selection in post-event RSRA.

Spatial Cross-Correlation Models for Absolute and Relative Spectral Input Energy Parameters Based on Geostatistical Tools

ABSTRACTIn recent years, energy-based seismic design methodology has received increasing attention because it takes into account not only the force and displacement behavior of a structure but also the cumulative damage effect caused by seismic loading. Specifically, as a fundamental parameter, input energy parameters (both absolute and relative measures) are directly related to the cumulative damage potential; therefore, they are commonly used in energy-based seismic design and seismic risk assessment. This study thus proposes new spatial cross-correlation models for absolute and relative elastic input energy parameters, using 2219 ground-motion records selected from 12 earthquake events. The normalized within-event residuals for both absolute and relative measures are first calculated. Semivariogram analysis is then conducted to quantify the spatial correlation of residuals for the input energy parameters at multiple sites and multiple periods. The linear model of coregionalization (LMC) approach is adopted to fit the empirical data; it is observed that the proposed LMC-based function performs reasonably well in capturing the spatial variability of the input energy measures. The influence of regional site conditions on the spatial cross correlation of input energy parameters is also investigated, and generic models are proposed using the averaged standardized coregionalization matrices of 12 events. The spatial cross-correlation models developed for input energy parameters can be used in regional seismic risk assessment within an energy-based framework.

Re-thinking site amplification in regional seismic risk assessment

Probabilistic assessment of seismic hazard and risk over a geographical region presents the modeler with challenges in the characterization of the site amplification that are not present in site-specific assessment. Using site-to-site residuals from a ground motion model fit to observations from the Japanese KiK-net database, correlations between measured local amplifications and mappable proxies such as topographic slope and geology are explored. These are used subsequently to develop empirical models describing amplification as a direct function of slope, conditional upon geological period. These correlations also demonstrate the limitations of inferring 30-m shearwave velocity from slope and applying them directly into ground motion models. Instead, they illustrate the feasibility of deriving spectral acceleration amplification factors directly from sets of observed records, which are calibrated to parameters that can be mapped uniformly on a regional scale. The result is a geologically calibrated amplification model that can be incorporated into national and regional seismic hazard and risk assessment, ensuring that the corresponding total aleatory variability reflects the predictive capability of the mapped site proxy.

Influence of intensity measure selection on simulation-based regional seismic risk assessment

This study investigates the influence of intensity measure (IM) selection on simulation-based regional seismic risk assessment (RSRA) of spatially distributed structural portfolios. First, a co-simulation method for general spectral averaging vector IMs is derived. Then a portfolio-level surrogate demand modeling approach, which incorporates the seismic demand estimation of the non-collapse and collapse states, is proposed. The derived IM co-simulation method enables the first comparative study of different IMs, including the conventional IMs and some more advanced scalar and vector IMs, in the context of RSRA. The influence of IM selection on the predictive performance of the portfolio-level surrogate demand models, as well as on the regional seismic risk estimates, is explored based on a virtual spatially distributed structural portfolio subjected to a scenario earthquake. The results of this study provide pertinent insights in surrogate demand modeling, IM co-simulation and selection, which can facilitate more accurate and reliable regional seismic risk estimates.

From rapid visual survey to multi-hazard risk prioritisation and numerical fragility of school buildings

Abstract. Regional seismic risk assessment is paramount in earthquake-prone areas, for instance, to define and implement prioritisation schemes for earthquake risk reduction. As part of the Indonesia School Programme to Increase Resilience (INSPIRE), this paper proposes an ad hoc rapid-visual-survey form, allowing one to (1) calculate the newly proposed INSPIRE seismic risk prioritisation index, which is an empirical proxy for the relative seismic risk of reinforced concrete (RC) buildings within a given building portfolio; (2) calculate the Papathoma Tsunami Vulnerability Assessment (PTVA) index, in any of its variations; (3) define one or more archetype buildings representative of the analysed portfolio; (4) derive detailed numerical models of the archetype buildings, provided that the simulated design is used to cross-check the model assumptions. The proposed INSPIRE index combines a baseline score, calibrated based on fragility curves, and a performance modifier, calibrated through the analytic hierarchy process (AHP) to minimise subjectivity. An attempt to define a multi-hazard prioritisation scheme is proposed, combining the INSPIRE and PTVA indices. Such a multi-level framework is implemented for 85 RC school buildings in Banda Aceh, Indonesia, the most affected city by the 2004 Indian Ocean earthquake–tsunami sequence. As part of the proposed framework, two archetype buildings representative of the entire portfolio are defined based on the collected data. Their seismic performance is analysed by means of non-linear static analyses, using both the analytical simple lateral mechanism analysis (SLaMA) method and numerical finite-element pushover analyses to investigate the expected plastic mechanisms and derive displacement/drift thresholds to define appropriate damage states. Finally, non-linear dynamic analyses are performed to derive fragility curves for the archetype buildings. This paper demonstrates the effectiveness of the INSPIRE data collection form and proposed index in providing a rational method to derive seismic risk prioritisation schemes and in allowing the definition of archetype buildings for more detailed evaluations/analyses.

Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes

Recent researches are directed towards the regional seismic risk assessment of structures based on a bridge inventory analysis. The framework for traditional regional risk assessments consists of grouping the bridge classes and generating fragility relationships for each bridge class. However, identifying the bridge attributes that dictate the statistically different performances of bridges is often challenging. These attributes also vary depending on the demand parameter under consideration. This paper suggests a multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes. The proposed methodology helps identify the relative importance of each uncertain parameter on the fragility curves. Results from the case study of skewed box-girder bridges reveal that the ground motion intensity measure, span length, and column longitudinal reinforcement ratio have a significant influence on the seismic fragility of this bridge class.

Parameterized Seismic Fragility Curves for Curved Multi-frame Concrete Box-Girder Bridges Using Bayesian Parameter Estimation

ABSTRACTThis paper addresses the application of a Bayesian parameter estimation method to a regional seismic risk assessment of curved concrete bridges. For this purpose, numerical models of case-study bridges are simulated to generate multiparameter demand models of components, consisting of various uncertainty parameters and an intensity measure (IM). The demand models are constructed using a Bayesian parameter estimation method and combined with limit states to derive the parameterized fragility curves. These fragility curves are used to develop bridge-specific and bridge-class fragility curves. Moreover, a stepwise removal process in the Bayesian parameter estimation is performed to identify significant parameters affecting component demands.

Bridge classes for regional seismic risk assessment: Improving HAZUS models

Recent efforts are oriented towards the regional risk assessment of bridge inventories by grouping bridge classes which are expected to have similar performance or damage measures during a seismic event. Although HAZUS represents the current state of the art in grouping the bridge classes, it suffers many drawbacks such as grouping based on traditional subjective approach, failure in explicitly addressing the effect of design and geometric attributes, and neglecting the effect of abutment type. A critical review of the HAZUS grouping and associated fragilities highlights the need for a more refined sub-binning of bridge classes for a reliable estimate of the seismic risk. Towards the objective of a performance-based grouping of bridge classes, a new grouping technique rooted in the statistical technique called analysis of variance is suggested in this paper. The approach is used to improve the HAZUS grouping through the case studies of California box-girder bridges. The proposed approach identifies more sub-classes than the HAZUS grouping, and the significance of the proposed grouping is demonstrated through the comparison of fragility curves of three bridge classes identified by the proposed grouping methodology.

Performance‐based grouping methods of bridge classes for regional seismic risk assessment: Application of ANOVA, ANCOVA, and non‐parametric approaches

SummaryOne of the key tasks to enable a regional risk assessment is to group structures with similar seismic performances and generate fragility curves representative of the grouped structures. The grouping has been traditionally performed based primarily on engineering judgment and prior experience. This paper (i) presents an overview of various statistical techniques such as analysis of variance, analysis of covariance, and Kruskal–Wallis test for grouping the bridges of similar performance; (ii) compares the groupings that emerge from the various grouping techniques; and (iii) identifies the method that has more statistical power in creating bridge sub‐classes of distinct structural performance. The grouping is achieved by comparing the structural responses of bridge classes obtained from the non‐linear time history analysis of bridges. The relative merits of these grouping techniques are discussed with the case study of box‐girder bridges in California. Copyright © 2017 John Wiley & Sons, Ltd.

ANCOVA-based grouping of bridge classes for seismic fragility assessment

Fragility curves that can be applicable for vulnerability modeling of various configurations of structures, such as bridges, are required to facilitate regional seismic risk assessment. However, it is cumbersome and time-consuming to develop unique fragility curves for each structure across a regional portfolio. One strategy that has been adopted to address this challenge is to group bridges into classes with similar design or structural performance. Traditionally, this grouping has been performed based on a relatively subjective identification of sub-classes. However, such an identification leads to a number of bridge classes with unwarranted grouping. To overcome this limitation, a new grouping technique based on an analysis of covariance on probabilistic seismic demand models is suggested in this paper. The proposed analysis of covariance based grouping helps to identify important attribute-related parameters and to create distinct bridge sub-classes. The effectiveness of the proposed approach is demonstrated in this paper through case studies of four concrete box-girder bridges in California. This is the first systematic approach to sub-binning bridge classes for the regional risk assessment. The results suggest that the proposed grouping method significantly reduces bridge sub-classes from all possible sub-class combinations. The proposed method is relevant and can be applicable to other bridge types.

Regional Seismic Risk Assessment of Bridge Network in Charleston, South Carolina

This article presents the results of a seismic risk assessment of the bridge network in Charleston, South Carolina and the surrounding counties to support emergency planning efforts, and for prioritization of bridge retrofit. This study includes an inventory analysis of the approximately 375 bridges in the Charleston area, and convolution of the seismic hazard with fragility curves analytically derived for classes of bridges common to this part of the country. State-of-the-art bridge fragility curves and replacement cost estimates based on region-specific data are used to obtain economic loss estimates. The distribution of potential bridge damage and economic losses are evaluated for several scenario events in order to aid in the identification of emergency routes and assess areas for investment in retrofit. This article also evaluates the effect of uncertainty on the resulting predicted economic losses. The findings reveal that while the risk assessment is very sensitive to both the assumed fragility curves and damage ratios, the estimate of total expected economic losses is more sensitive to the vast differences in damage ratio models considered.

Retrofitted Bridge Fragility Analysis for Typical Classes of Multispan Bridges

Retrofitted bridge fragility curves provide a powerful tool for assessing the effect of retrofit measures on the seismic performance of different bridge types under a range of loading levels. Traditional methods for retrofit assessment typically evaluate the effectiveness of retrofit based on the performance of individual components. However, the use of fragility curves for retrofitted bridges has the ability to capture the impact of retrofit on the bridge system vulnerability. Using three-dimensional nonlinear analysis, fragility curves are developed for four common classes of multispan bridges and five retrofit methods. The results show that the effectiveness of retrofit is a function of bridge type and damage state. General conclusions of the influence of the different retrofit measures on the fragility of each class of typical bridges in the Central and Southeastern United States, as well as the fragility parameters, are presented. The results from this work can be used to enhance regional seismic risk assessment and can form the basis for retrofit cost-benefit studies.

Sensitivity of Seismic Response and Fragility to Parameter Uncertainty

As the use for regional seismic risk assessment increases, the need for reliable fragility curves for portfolios (or classes) of structures becomes more important. Fragility curves for portfolios of structures have the added complexity of having to deal with the uncertainty in geometric properties, along with the typical uncertainties such as material or component response parameters. Analysts are challenged with selecting a prudent level of uncertainty treatment while balancing the simulation and computational effort. In order to address this question, this study first evaluates the modeling parameters which significantly affect the seismic response of an example class of retrofitted bridges. Further, the relative importance of the uncertainty in these modeling parameters, gross geometries, and ground motions is assessed. The study reveals that savings in simulation and computational effort in fragility estimation may be achieved through a preliminary screening of modeling parameters. However, the propagation of these potentially variable parameters tends to be overshadowed by the uncertainty in the ground motion and base geometry of the structural class.

Regional seismic risk in British Columbia — damage and loss distribution in Victoria and Vancouver

This paper presents the results of regional seismic risk assessment studies that were carried out for two cities in southwestern British Columbia, Vancouver and Victoria. Ground shaking intensity in the area was obtained using the seismic source zones delineated by the Geological Survey of Canada for a probability level of 10% chance of exceedance in 50 years. Building inventories were compiled by aggregating data from sidewalk surveys as well as municipal databases. Modified Mercalli intensity-based damage matrices that relate the level of ground shaking to the amount of damage expected in different types of structures were used to estimate damage to structural and non-structural components of buildings. Estimated damage and loss distributions were mapped on a block-by-block basis. The historic sections of the cities were estimated to have damage levels between 10% and 30% of the replacement cost, while in the rest of the cities the estimated damage was generally in the 5% to 10% range. The results show the estimated economic loss distribution is considerably different from the damage distribution. Although the older neighbourhoods of the cities are expected to suffer highest amount of damage, the highest amount of economic loss is estimated to occur in areas with concentration of concrete high-rise buildings.Key words: seismic hazard, seismic risk, vulnerability, earthquake, damage, loss, probability, modified Mercalli intensity.

A comparison of satellite laser ranging and seismicity data in the Aegean Region

In this study we compare the strain rates obtained from earthquake moment tensors in the time interval 1911–92 in Greece with measured velocities of SLR stations relative to Eurasia. We find that the SLR measurements do not require strain different in orientation from that seen in the earthquakes, but simply more of it. The clearest deficit is in the SE Aegean where earthquakes this century can account for virtually none of the measured SLR strain rates. By contrast, in the northern Aegean and NW Turkey the earthquakes can account for a substantial part (>50%) of the measured strain rates. By using both geodetic and seismic data to localize apparent deficits in seismically‐released strain, this study represents a first step towards using space‐based geodesy for crude regional seismic risk assessment.