Methodology for Development of Physics-Based Tsunami Fragilities

Attary, Navid; Lindt, John W. van de; Unnikrishnan, Vipin U.; Barbosa, André R.; Cox, Daniel T.

doi:10.1061/(asce)st.1943-541x.0001715

Cited by 49 publications

(45 citation statements)

References 26 publications

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“…Therefore, the momentum flow based fragility curve is perhaps a more applicable model to reflect the complex building response during the inundation process rather than the flow depth based fragility curve. On the other hand, it may be necessary to replace fragilities curves with a fragility surface comprised of depth and velocity as independent variables (e.g., Attary et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis

Park

Cox

Barbosa

2017

Coastal Engineering

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Annual exceedance probabilities of the maximum tsunami inundation depth, h Max , and momentum flux, M Max , conditional on a full-rupture event of the Cascadia Subduction Zone (CSZ) were used to estimate the probability of building damage using a fragility analysis at Seaside, Oregon. Tax lot data, Google Street View, and field reconnaissance surveys were used to classify the buildings in Seaside and to correlate building typologies with existing fragility curves according to the construction material, number of stories, and building seismic design level based on the date of construction. A fragility analysis was used to estimate the damage probability of buildings for 500-, 1,000-, and 2,500-year exceedance probabilities conditioned on a full-rupture CSZ event. Finally, the sensitivity of building damage was estimated for both the aleatory and epistemic uncertainties involved in the process of damage estimation. Probable damage estimates from the fragility curves based on h Max and on M Max both generally show higher damage probability for structures that are wooden and closer to the shoreline than those that are reinforced concrete (RC) and further landward of the shoreline. However, a relatively high and somewhat unrealistic damage probability was found at the river and creek region from the fragility curve analysis using h Max. Within 500 m from the shoreline, wood structure damage shows significant sensitivity to the aleatory uncertainty of the tsunami generation from the CSZ event. On the other hand, RC structure damage showed equal sensitivity to the aleatory uncertainty of the tsunami generation as well as the epistemic uncertainties due to the numerical modeling of the tsunami inundation (friction), the building classification (material and date of construction), and the type of fragility curves (depth or momentum flux type curves). Further from the shoreline, the wood structures showed similar aleatory and epistemic uncertainties, qualitatively similar to the RC structure sensitivity closer to the shoreline.

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Section: Discussionmentioning

confidence: 99%

Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis

Park

Cox

Barbosa

2017

Coastal Engineering

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“…collapse and complete damage) for given IM. The fragility functions can be derived empirically (De Risi et al 2017) as well as analytically (Attary et al 2016;Petrone et al 2017).…”

Section: Probabilistic Tsunami Loss Estimation Framework Formulationmentioning

confidence: 99%

“…It is also clear that inspection of the average trend as well as variability of the key tsunami hazard parameters provides valuable insight in developing local tsunami evacuation strategies. From a tsunami engineering perspective, simulated tsunami waveforms are particularly useful for carrying out advanced structural analyses subjected to tsunami wave loading to develop analytical tsunami fragility models (Attary et al 2016;Petrone et al 2017). …”

Section: Tsunami Hazard Analysismentioning

confidence: 99%

“…On the other hand, current tsunami vulnerability assessment is largely empirical (Tarbotton et al 2015;Macabuag et al 2016), resulting in difficulties when the PBTE framework is applied to geographical regions where empirical tsunami damage data (and thus relevant tsunami fragility models) are lacking. This limitation can be overcome by developing analytical tsunami fragility models (Park et al 2012;Attary et al 2016;Petrone et al 2017), similarly to the seismic vulnerability counterpart. Importantly, one of the goals of this work is to bring both PBEE and PBTE on the coherent computational framework (De Risi and Goda 2016).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Tsunami Loss Estimation Methodology: Stochastic Earthquake Scenario Approach

Goda

Risi

2017

Earthquake Spectra

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms The Professional Journal of the Earthquake Engineering Research Institute PREPRINTThis preprint is a PDF of a manuscript that has been accepted for publication in Earthquake Spectra. It is the final version that was uploaded and approved by the author(s). While the paper has been through the usual rigorous peer review process for the Journal, it has not been copyedited, nor have the figures and tables been modified for final publication. Please also note that the paper may refer to online Appendices that are not yet available.We have posted this preliminary version of the manuscript online in the interest of making the scientific findings available for distribution and citation as quickly as possible following acceptance. However, readers should be aware that the final, published version will look different from this version and may also have some differences in content.The DOI for this manuscript and the correct format for citing the paper are given at the top of the online (html) abstract.Once the final, published version of this paper is posted online, it will replace the preliminary version at the specified DOI. Probabilistic Tsunami Loss Estimation

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“…These results FIGURE 10 | Annual exceedance probability (AEP) of hmax (A) and (M F )max (B) at Points 1 (black solid), 2 (red dash), and 3 (blue dash-dot). are helpful to understand realistic input conditions of flow depth and velocity fields for different Fr regime that can be used to develop the tsunami fragility curves, which utilize the random combinations of h max and flow velocity in the generation of fragility curves (Attary et al, 2017a;Attary et al, 2017b;Alam et al, submitted 2 ). In addition, it is worth noting that the maximum flow depth and momentum flux typically do not occur at the same time (Park et al, 2013).…”

Section: Tsunami Intensitymentioning

confidence: 99%

Probabilistic Seismic and Tsunami Hazard Analysis Conditioned on a Megathrust Rupture of the Cascadia Subduction Zone

Park

Cox

Alam

et al. 2017

Front. Built Environ.

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This paper presents a methodology for probabilistic hazard assessment for the multihazard seismic and tsunami phenomena [probabilistic seismic and tsunami hazard analysis (PSTHA)]. For this work, a full-rupture event along the Cascadia subduction zone is considered and the methodology is applied to a study area of Seaside, Oregon, which is located on the US Pacific Northwest coast. In this work, the annual exceedance probabilities (AEPs) of the tsunami intensity measures (IMs) are shown to be qualitatively dissimilar to the IMs of the seismic ground motion in the study area. Specifically, the spatial gradients for the tsunami IM are much stronger across the length scale of the study area owing to the physical differences of wave propagation and energy dissipation of the two mechanisms. Example results of probabilistic seismic hazard analysis and probabilistic tsunami hazard analysis are shown for three observation points in the study area of Seaside. For the seismic hazard, the joint mean annual rate of exceedance of IMs shows similar trends for the three observation points, even though for a given observation point there is a large scatter between two ground-motion IMs analyzed, which were peak ground acceleration (PGA) and spectral acceleration at a period of vibration of 0.3 s, i.e., PGA and S a (T 1 = 0.3 s). For the tsunami hazard, the joint AEP of maximum flow depth (h max ) and maximum momentum flux ((M F ) max ) shows a high correlation between the two IMs in the study area. The joint AEP at each of the three observation points follows a particular Froude number (Fr) due to the local site-specific conditions rather than the distributions of fault slip distributions used to generate the scenarios that are the basis of the AEP maps developed. The joint probability distribution of h max and (M F ) max throughout the study region falls between 0.1 ≤ Fr < 1.0 (i.e., the flow is subcritical), regardless of return interval (500-, 1,000-, and 2,500-year). However, the peak of the joint probability distribution with respect to h max and (M F ) max varies with the return interval, and the largest values of h max and (M F ) max were observed with the highest return intervals (2,500 years) as would be expected. The results of the PSTHA can be the basis for a probabilistic multi-hazard damage and loss assessment and help to evaluate the uncertainties of the multi-hazard assessments.

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Methodology for Development of Physics-Based Tsunami Fragilities

Cited by 49 publications

References 26 publications

Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis

Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis

Probabilistic Tsunami Loss Estimation Methodology: Stochastic Earthquake Scenario Approach

Probabilistic Seismic and Tsunami Hazard Analysis Conditioned on a Megathrust Rupture of the Cascadia Subduction Zone

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