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1. A new perspective on seismic intensity measures (IMs)

   Grigoriu, M. (August 2019, SEMC 2019)

   The usefulness of current intensity measures (IMs) and fragilities are assessed in a setting in which the probability law of the seismic ground acceleration process is known. It is shown that typical demand parameters and IMs are weakly dependent so that fragilities defined as functions of these measures provide limited information for seismic design. 

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2. An earthquake-source-based metric for seismic fragility analysis

   https://doi.org/10.1007/s10518-018-0341-9  

   Radu, Alin; Grigoriu, Mircea (February 2018, Bulletin of Earthquake Engineering)

   The seismic fragility of a system is the probability that the system enters a damage state under seismic ground motions with specified characteristics. Plots of the seismic fragilities with respect to scalar ground motion intensity measures are called fragility curves. Recent studies show that fragility curves may not be satisfactory measures for structural seismic performance, since scalar intensity measures cannot comprehensively characterize site seismicity. The limitations of traditional seismic intensity measures, e.g., peak ground acceleration or pseudo-spectral acceleration, are shown and discussed in detail. A bivariate vector with coordinates moment magnitude m and source-to-site distance r is proposed as an alternative seismic intensity measure. Implicitly, fragility surfaces in the (m, r)-space could be used as graphical representations of seismic fragility. Unlike fragility curves, which are functions of scalar intensity measures, fragility surfaces are characterized by two earthquake-hazard parameters, (m, r). The calculation of fragility surfaces may be computationally expensive for complex systems. Thus, as solutions to this issue, a bi-variate log-normal parametric model and an efficient calculation method, based on stochastic-reduced-order models, for fragility surfaces are proposed. 

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3. Are seismic fragility curves fragile?

   https://doi.org/doi.org/10.1016/j.probengmech.2020.103115  

   Grigoriu, M. and (January 2021, Probabilistic engineering mechanics)

   null (Ed.)

   A hypothetical seismic site is constructed for which the probability law of the seismic ground acceleration process 𝑋(𝑡) is specified. Since the seismic hazard is known, the performance of the incremental dynamic analysis- (IDA) and multiple stripe analysis- (MSA) based fragilities, which are used extensively in Earthquake Engineering, can be assessed without ambiguity. It is shown that the IDA- and MSA-based fragilities are unsatisfactory for moderate and large seismic events, are sensitive to the particular parameters used for their construction, and may or may not improve with the sample size. Also, the usefulness of the optimization algorithms for selecting ground motions records is questionable. 

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4. Fragility modeling practices and their implications on risk and resilience analysis: From the structure to the network scale

   https://doi.org/10.1177/87552930231219220  

   Rincon, Raul; Padgett, Jamie_Ellen (January 2024, Earthquake Spectra)

   Although fragility function development for structures is a mature field, it has recently thrived on new algorithms propelled by machine learning (ML) methods along with heightened emphasis on functions tailored for community- to regional-scale application. This article seeks to critically assess the implications of adopting alternative traditional and emerging fragility modeling practices within seismic risk and resilience quantification to guide future analyses that span from the structure to infrastructure network scale. For example, this article probes the similarities and differences in traditional and ML techniques for demand modeling, discusses the shift from one-parameter to multiparameter fragility models, and assesses the variations in fragility outcomes via statistical distance concepts. Moreover, the previously unexplored influence of these practices on a range of performance measures (e.g. conditional probability of damage, risk of losses to individual structures, portfolio risks, and network recovery trajectories) is systematically evaluated via the posed statistical distance metrics. To this end, case studies using bridges and transportation networks are leveraged to systematically test the implications of alternative seismic fragility modeling practices. The results show that, contrary to the classically adopted archetype fragilities, parameterized ML-based models achieve similar results on individual risk metrics compared to structure-specific fragilities, promising to improve portfolio fragility definitions, deliver satisfactory risk and resilience outcomes at different scales, and pinpoint structures whose poor performance extends to the global network resilience estimates. Using flexible fragility models to depict heterogeneous portfolios is expected to support dynamic decisions that may take place at different scales, space, and time, throughout infrastructure systems. 

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5. Spatial variability of site effects and its correlation with site response in Japan

   https://doi.org/10.3208/jgssp.v10.P2-25  

   Lorenzo-Velazquez, Cristina; Cabas, Ashly (January 2024, Japanese Geotechnical Society Special Publication)

   Local soil conditions depict an important role in regional seismic hazard assessments due to their influence on earthquake-induced ground shaking and deformation. The different levels of damage and site response at nearby locations correlate to site and geologic conditions variability, as has been reported after past earthquakes. Evaluating spatially variable ground motions (GMs) is key for earthquake reconnaissance efforts and regional seismic hazard assessments. This study focuses on the evaluation of spatial correlations in site parameters (e.g. time-averaged shear-wave velocity to a depth of 30 meters) at Kiban-Kyoshin Network (KiK-net), and their comparison to the observed spatial correlation residuals from ground motion intensity measures (IMs) from the Mw9.1 Tohoku earthquake. Current spatial correlation models treat site effects either as a fixed amplification factor or as randomized amplifications, but site effects are neither fixed nor random. Hence, geostatistical methods are used here to estimate spatial correlations between parameters that control site response and integrate their effects on resulting spatially variable ground motions. In this work, we evaluate the significance of the spatial correlation for different site parameters with respect to the GM amplification IMs residuals. 

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