A site‐consistent method to quantify sufficiency of alternative IMs in relation to PSDA

Summary In probabilistic seismic demand analysis, evaluation of the sufficiency of an intensity measure (IM) is an important criterion to avoid biased assessment of the demand hazard. However, there exists no metric to quantify the degree of sufficiency as per the criterion of Luco and Cornell (2007). This paper proposes a site-specific unified measure for degree of sufficiency from all seismological parameters under consideration using a total information gain metric. This unified metric for sufficiency supports not only comparison of the performance of different IMs given a response quantity but also assessment of the performance of a particular IM across different response quantities. The proposed sufficiency metric was evaluated for a 4-story steel moment frame building, and the influence of ground motion selection on the degree of sufficiency was investigated. It was observed that ground motion selection can have a significant impact on IM sufficiency. Because computing the total information gain requires continuous deaggregation across the IM space, an approximate deaggregation technique that allows for a more practical estimation of marginal deaggregation probabilities is proposed. It is expected that the total information gain metric proposed in this paper will aid in understanding the efficiency-sufficiency relation, thus enabling the selection of a proper scalar IM for a given site and application in probabilistic seismic demand analysis.

[1]  Tiziana Rossetto,et al.  Spectral shape proxies and simplified fragility analysis of mid-rise reinforced concrete buildings , 2015 .

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

[3]  Nicolas Luco,et al.  Structure-Specific Scalar Intensity Measures for Near-Source and Ordinary Earthquake Ground Motions , 2007 .

[4]  Brendon A. Bradley,et al.  Intensity measures for the seismic response of pile foundations , 2009 .

[5]  J. Baker,et al.  Statistical Tests of the Joint Distribution of Spectral Acceleration Values , 2008 .

[6]  W. Silva,et al.  Stochastic Modeling of California Ground Motions , 2000 .

[7]  Luis Ibarra,et al.  Hysteretic models that incorporate strength and stiffness deterioration , 2005 .

[8]  T. Jordan,et al.  OpenSHA: A Developing Community-modeling Environment for Seismic Hazard Analysis , 2003 .

[9]  Dimitrios Vamvatsikos,et al.  Intensity measure selection for vulnerability studies of building classes , 2015 .

[10]  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 .

[11]  Luis Esteva,et al.  Comparing the adequacy of alternative ground motion intensity measures for the estimation of structural responses , 2004 .

[12]  Victor E. Saouma,et al.  Probabilistic seismic demand model and optimal intensity measure for concrete dams , 2016 .

[13]  Dimitrios Vamvatsikos,et al.  Incremental dynamic analysis , 2002 .

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

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

[16]  Fabio Freddi,et al.  Probabilistic seismic demand modeling of local level response parameters of an RC frame , 2016, Bulletin of Earthquake Engineering.

[17]  B. Bradley.  A generalized conditional intensity measure approach and holistic ground‐motion selection , 2010 .

[18]  Dimitrios G. Lignos,et al.  An efficient method for estimating the collapse risk of structures in seismic regions , 2013 .

[19]  Vahid Jahangiri,et al.  Intensity measures for the assessment of the seismic response of buried steel pipelines , 2016, Bulletin of Earthquake Engineering.

[20]  Jack W. Baker,et al.  Conditional spectrum‐based ground motion selection. Part I: Hazard consistency for risk‐based assessments , 2013 .

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

[22]  Jack W. Baker,et al.  Quantitative Classification of Near-Fault Ground Motions Using Wavelet Analysis , 2007 .

[23]  Fatemeh Jalayer,et al.  Bayesian Cloud Analysis: efficient structural fragility assessment using linear regression , 2014, Bulletin of Earthquake Engineering.

[24]  Dimitrios Vamvatsikos,et al.  Vector and Scalar IMs in Structural Response Estimation, Part I: Hazard Analysis , 2016 .

[25]  Jack W. Baker,et al.  A Computationally Efficient Ground-Motion Selection Algorithm for Matching a Target Response Spectrum Mean and Variance , 2011 .

[26]  Michele Barbato,et al.  Probabilistic seismic demand model for pounding risk assessment , 2016 .