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.

[1]  G. Box,et al.  On the Experimental Attainment of Optimum Conditions , 1951 .

[2]  S. Popovics.  A numerical approach to the complete stress-strain curve of concrete , 1973 .

[3]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[4]  C. Allin Cornell,et al.  Structural Seismic Demand Analysis: Consideration of "Collapse" , 2000 .

[5]  M. Fardis,et al.  Deformations of Reinforced Concrete Members at Yielding and Ultimate , 2001 .

[6]  Kevin R. Mackie,et al.  Probabilistic Seismic Demand Model for California Highway Bridges , 2001 .

[7]  Gregory G. Deierlein,et al.  Development of a two-parameter seismic intensity measure and probabilistic assessment procedure , 2001 .

[8]  C. Allin Cornell,et al.  Probabilistic Basis for 2000 SAC Federal Emergency Management Agency Steel Moment Frame Guidelines , 2002 .

[9]  Stephanie E. Chang.  Evaluating disaster mitigations: methodology for urban infrastructure systems , 2003 .

[10]  Peeranan Towashiraporn,et al.  Building Seismic Fragilities Using Response Surface Metamodels , 2004 .

[11]  B. G. Nielson.  Analytical Fragility Curves for Highway Bridges in Moderate Seismic Zones , 2005 .

[12]  Dimitrios Vamvatsikos,et al.  Developing efficient scalar and vector intensity measures for IDA capacity estimation by incorporating elastic spectral shape information , 2005 .

[13]  Yong Lu,et al.  Probabilistic Drift Limits and Performance Evaluation of Reinforced Concrete Columns , 2005 .

[14]  Tsuyoshi Takada,et al.  Macrospatial Correlation Model of Seismic Ground Motions , 2005 .

[15]  J. Bommer,et al.  Modelling Seismic Hazard in Earthquake Loss Models with Spatially Distributed Exposure , 2006 .

[16]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[17]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[18]  Nicolas Luco,et al.  Probabilistic seismic demand analysis using advanced ground motion intensity measures , 2007 .

[19]  Paolo Bazzurro,et al.  Modeling spatial correlation of ground motion Intensity Measures for regional seismic hazard and portfolio loss estimation , 2007 .

[20]  Katsuichiro Goda,et al.  Estimation of Seismic Loss for Spatially Distributed Buildings , 2008 .

[21]  J. Baker,et al.  Correlation of Spectral Acceleration Values from NGA Ground Motion Models , 2008 .

[22]  J. Baker,et al.  Prediction of inelastic structural response using an average of spectral accelerations , 2008 .

[23]  G. Atkinson,et al.  Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01 s and 10.0 s , 2008 .

[24]  C. Cornell,et al.  Vector-valued Intensity Measures Incorporating Spectral Shape For Prediction of Structural Response , 2008 .

[25]  Dawn E. Lehman,et al.  Lumped-Plasticity Models for Performance Simulation of Bridge Columns , 2008 .

[26]  Reginald DesRoches,et al.  Selection of optimal intensity measures in probabilistic seismic demand models of highway bridge portfolios , 2008 .

[27]  H. Hong,et al.  Spatial correlation of peak ground motions and response spectra , 2008 .

[28]  J. Baker,et al.  Correlation model for spatially distributed ground‐motion intensities , 2009 .

[29]  Julian J. Bommer,et al.  Empirical Equations for the Prediction of the Significant, Bracketed, and Uniform Duration of Earthquake Ground Motion , 2009 .

[30]  Vesna Terzic.  Post-earthquake traffic capacity of modern bridges in California. , 2009 .

[31]  Gail M. Atkinson,et al.  Probabilistic Characterization of Spatially Correlated Response Spectra for Earthquakes in Japan , 2009 .

[32]  Barbara Ferracuti,et al.  Response Surface with random factors for seismic fragility of reinforced concrete frames , 2010 .

[33]  Kenneth W. Campbell,et al.  A Ground Motion Prediction Equation for the Horizontal Component of Cumulative Absolute Velocity (CAV) Based on the PEER-NGA Strong Motion Database , 2010 .

[34]  Reginald DesRoches,et al.  Regional Seismic Risk Assessment of Bridge Network in Charleston, South Carolina , 2010 .

[35]  Friedemann Wenzel,et al.  Influence of ground-motion correlation on probabilistic assessments of seismic hazard and loss: sensitivity analysis , 2011 .

[36]  Frank McKenna,et al.  OpenSees: A Framework for Earthquake Engineering Simulation , 2011, Computing in Science & Engineering.

[37]  John B. Mander,et al.  Stress-Block Parameters for Unconfined and Confined Concrete Based on a Unified Stress-Strain Model , 2011 .

[38]  Reginald DesRoches,et al.  Fractional order intensity measures for probabilistic seismic demand modeling applied to highway bridges , 2012 .

[39]  Reginald DesRoches,et al.  A comparison of pre- and post-seismic design considerations in moderate seismic zones through the fragility assessment of multispan bridge classes , 2012 .

[40]  Farzin Zareian,et al.  Analyzing the Sufficiency of Alternative Scalar and Vector Intensity Measures of Ground Shaking Based on Information Theory , 2012 .

[41]  Simona Esposito,et al.  Spatial Correlation of Spectral Acceleration in European Data , 2012 .

[42]  Sonia E. Ruiz,et al.  Comparing vector-valued intensity measures for fragility analysis of steel frames in the case of narrow-band ground motions , 2012 .

[43]  Kenneth W. Campbell,et al.  A Comparison of Ground Motion Prediction Equations for Arias Intensity and Cumulative Absolute Velocity Developed Using a Consistent Database and Functional Form , 2012 .

[44]  Jack W. Baker,et al.  A spatial cross‐correlation model of spectral accelerations at multiple periods , 2013 .

[45]  Leonardo Dueñas-Osorio,et al.  Surrogate modeling and failure surface visualization for efficient seismic vulnerability assessment of highway bridges , 2013 .

[46]  Leonardo Dueñas-Osorio,et al.  Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures, I: Methodology , 2014 .

[47]  W. Silva,et al.  NGA-West2 Database , 2014 .

[48]  Leonardo Dueñas-Osorio,et al.  Toward a uniform seismic risk design of reinforced concrete bridges: A displacement-based approach , 2014 .

[49]  Jamie E. Padgett,et al.  Seismic Damage Accumulation in Highway Bridges in Earthquake-Prone Regions , 2015 .

[50]  F. Mollaioli,et al.  Preliminary ranking of alternative scalar and vector intensity measures of ground shaking , 2015, Bulletin of Earthquake Engineering.

[51]  B. Bradley,et al.  Ground motion selection for scenario ruptures using the generalised conditional intensity measure (GCIM) method , 2015 .

[52]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[53]  Fatemeh Jalayer,et al.  Model updating and seismic loss assessment for a portfolio of bridges , 2015, Bulletin of Earthquake Engineering.

[54]  Paolo Bazzurro,et al.  Exploring the impact of spatial correlations and uncertainties for portfolio analysis in probabilistic seismic loss estimation , 2015, Bulletin of Earthquake Engineering.

[55]  Dimitrios G. Lignos,et al.  Average spectral acceleration as an intensity measure for collapse risk assessment , 2015 .

[56]  Dimitrios Vamvatsikos,et al.  Vector and Scalar IMs in Structural Response Estimation, Part II: Building Demand Assessment , 2016 .

[57]  Halil Sezen,et al.  Analytical fragility assessment using unscaled ground motion records , 2017 .

[58]  J. Padgett,et al.  Adaptive IMs for improved seismic demand modeling of highway bridge portfolios , 2017 .

[59]  Jong-Su Jeon,et al.  Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes , 2018 .

[60]  Abdollah Shafieezadeh,et al.  A posteriori optimal intensity measures for probabilistic seismic demand modeling , 2018, Bulletin of Earthquake Engineering.