Uncertainty and Spatial Correlation of Earthquake Ground Motion in Taiwan

Sokolov, Vladimir; Wenzel, Friedemann; Jean, Wen-Yu; Wen, Kuo‐Liang

doi:10.3319/tao.2010.05.03.01(t)

Cited by 63 publications

(51 citation statements)

References 48 publications

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“…Boore et al 2003; Wang and Takada 2005;Sokolov et al 2010), and that the distances over which the correlations may be significant are generally greater for the long period characteristics of the ground motion (Jayaram and Baker 2009). In recent years these observations have proceeded to form the basis for the development of models of spatial cross-correlation to describe the correlation between different ground motion IMs across sites separated over a distance (e.g.…”

Section: Introductionmentioning

confidence: 94%

Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation

Weatherill

Silva

Crowley

et al. 2015

Bull Earthquake Eng

120

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The significant potential for human and economic losses arising from earthquakes affecting urban infrastructure has been demonstrated by many recent events such as, for example, L' Aquila (2009), Christchurch (2011) and Tohoku (2012. Within the current practice of seismic loss estimation in both academic and industry models, the modelling of spatial variability of the earthquake ground motion input across a region, and its corresponding influence upon portfolios of heterogeneous building types, may be oversimplified. In particular, the correlation properties that are well-known in observations of ground motion intensity measures (IMs) may not always be fully represented within the probabilistic modelling of seismic loss. Using a case study based on the Tuscany region of Italy, the impacts of including spatially cross-correlated random fields of different ground motion IMs are appraised at varying spatial resolutions. This case study illustrates the impact on the resulting seismic loss when considering synthetic aggregated portfolios over different spatial scales. Inclusion of spatial cross-correlation of IMs into the seismic risk analysis may often result in the likelihood of observing larger (and in certain cases smaller) losses for a portfolio distributed over a typical city scale, when compared against simulations in which the cross-correlation is neglected. It can also be seen that the degree to which the spatial correlations and cross-correlations can impact upon the loss estimates is sensitive to the conditions of the portfolio, particularly with respect to the spatial scale, the engineering properties of the different building types within the portfolio and the heterogeneity of the portfolio with respect to the types.

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Section: Introductionmentioning

confidence: 94%

Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation

Weatherill

Silva

Crowley

et al. 2015

Bull Earthquake Eng

120

View full text Add to dashboard Cite

show abstract

“…Europe, such as Northridge (1994) or Chi-Chi (1999). A few works have, instead, combined data from multiple events to obtain a unique estimate of correlation (e.g., 2008a, Goda andAtkinson, 2009;Goda and Atkinson, 2010;Sokolov et al, 2010). Different authors, for a given IM, provide different distance limits for correlation to disappear (i.e., distance beyond which IMs may be considered uncorrelated), and this is supposed to depend on the dataset considered, the GMPE chosen to compute residuals, and the working assumptions of the estimation.…”

Section: Introductionmentioning

confidence: 98%

“…For example, Goda and Atkinson (2009) investigated the effects of earthquake types (i.e., shallow and deep events) on correlation using datasets from K-NET and KiK-net Japanese strong-motion networks without finding any significant dependency. On the other hand, Sokolov et al (2010), starting from the strong-motion database of Taiwan Strong Motion Instrumentation Program (TSMIP) network, estimated correlation for various areas, site classes, and geological structures, asserting that a single generalized spatial model may not be adequate for all of Taiwan territory.…”

Section: Introductionmentioning

confidence: 99%

“…In some cases (e.g., Wang and Takada, 2005;Jayaram and Baker, 2009), existing GMPEs are used, while in others (e.g., Goda and Hong, 2008a;Goda and Atkinson, 2009;Sokolov et al, 2010), ad hoc fit on the chosen dataset is adopted. Generally, regression analysis used to develop prediction equations does not incorporate the correlation structure of residuals as a hypothesis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PGA and PGV Spatial Correlation Models Based on European Multievent Datasets

Esposito

Iervolino

2011

Bulletin of the Seismological Society of America

105

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Spatial modeling of ground motion intensity measures (IMs) is required for risk assessment of spatially distributed engineering systems. For example, when a lifeline system is of concern, classical site-specific hazard tools, which treat IMs at different locations independently, may not be adequate to accurately assess the seismic risk. In fact, in this case, modeling of ground motion as a random field is required; it basically consists of assigning a correlation structure to the IM of interest. This work focuses on semiempirical estimation of the correlation coefficient, as a function of intersite separation distance, between residuals with respect to ground motion prediction equations (GMPEs) of horizontal peak ground acceleration (PGA) and peak ground velocity (PGV). In particular, subsets of the European Strong-Motion Database (ESD) and the Italian Accelerometric Archive (ITACA) were employed to evaluate the intraevent residual correlation based on multiple earthquakes, considering different GMPEs fitted to the same records. The analyses were carried out through geostatistical tools, which enabled results to be found that are generally consistent between the two datasets. Correlation for PGV appears to attenuate more gradually with respect to PGA. In order to better understand the dependency of the results on the adopted estimation approach and dataset, some aspects related to the working hypotheses are critically discussed. Finally, estimated correlation models are used to develop illustrative applications of regional probabilistic seismic-hazard analysis.

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“…The integration of seismicity from different earthquake sources that is expressed in the form of conventional seismic hazard maps, is not applicable for the case of the post-earthquake traffic distribution, as the latter depends on the individual probability of operation of each network key component, which is in turn dependent on the specific seismic scenario examined and the corresponding spatial distribution of the Intensity Measures (IM) of interest [12], [13]. For this reason, hazard is herein assessed independently for each one of the m seismic sources potentially affecting the network and for a set of n different return periods.…”

Section: Seismic Hazard Analysismentioning

confidence: 99%

Seismic Resilience Assessment of the Western Macedonia Highway Network in Greece

Sextos¹,

Kilanitis

Kappos

et al. 2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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Retis-Risk is a comprehensive framework recently developed for the assessment and management of the seismic resilience of interurban roadway networks. In this paper, this framework is applied for the road network of the prefecture of Western Macedonia in Greece. Refined data concerning real traffic conditions and network topology were collected and a detailed network mapping was performed in GIS. Utilizing an ad-hoc developed software for implementing the holistic methodology, the as-built road network was assessed and the network components with the highest vulnerability and consequence were identified. To improve the loss assessment reliability, bridge-specific fragility curves were employed based on the refined FE modeling. Structural and traffic cost due to earthquake scenarios of different return period were then predicted. To identify effective loss mitigation measures, the whole process was repeated assuming two different pre-earthquake risk management strategies. The first one concerns a retrofit program tailored to selected (i.e., the most critical) network components, while the second one focuses on the beneficial effect of a better recovery planning. Results indicate the significant contribution of an effective risk management to the loss mitigation and network resilience improvement. Moreover, a sustainable health monitoring system was installed on Polymylos bridge to ensure satellite data transmission during an earthquake event to update the postearthquake recovery model, nearly in real time, with the measured spectral acceleration of the superstructure. The results indicate that the road network is adequately resilient, primarily due to its newly constructed infrastructure and its compliance to modern seismic standards. However, it consists an interesting application case demonstrating the applicability of the methodology and the major potential benefits of a holistic resilience-based management for the case similar intercity highway networks.

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Uncertainty and Spatial Correlation of Earthquake Ground Motion in Taiwan

Cited by 63 publications

References 48 publications

Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation

Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation

PGA and PGV Spatial Correlation Models Based on European Multievent Datasets

Seismic Resilience Assessment of the Western Macedonia Highway Network in Greece

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