Quantification of the Approximations Introduced by Assumptions on the Marginal Distribution of the Demand for Highway Bridge Fragility Analysis

Fragility analysis is one of the most popular tools for the computation of the probability of reaching each investigated limit state for a given value of the external load intensity. Because of its versatility and computational efficiency, fragility analysis is particularly appropriate for regional loss estimations and risk analyses. Several different techniques have been proposed in the literature to compute the fragility of buildings and bridges. However, most of these techniques rely on simplifications and assumptions which, in some cases, are made more for analytical convenience than for adherence to reality. This paper investigates the effect of three of these common assumptions made on the engineering demand parameters for the case of seismic fragility analyses of bridges: (1) the marginal distribution is lognormal, (2) the median of such distribution follows a power law of the seismic intensity measure of choice, (3) the dispersion of such distribution is constant for any value of the intensity measure. Numerical analyses have been performed running extensive Monte Carlo simulations on computational clusters, focusing on structural models of different complexity. The results suggest that assumption (1) is not realistic in general, but it does not induce a significant error on the final results, whereas assumptions (2) and (3) corrupt significantly the quality of the results, introducing substantial errors. A comprehensive methodology for the assessment of fragility curves for structural components and systems is then proposed, which does not rely on the above mentioned assumptions.

[1]  Nikos D. Lagaros,et al.  Computationally efficient seismic fragility analysis of geostructures , 2009 .

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

[3]  Reginald DesRoches,et al.  Seismic fragility methodology for highway bridges using a component level approach , 2006 .

[4]  Liang Chang,et al.  Bridge Seismic Retrofit Program Planning to Maximize Postearthquake Transportation Network Capacity , 2012 .

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

[6]  M. Grigoriu.  Simulation of stationary non-Gaussian translation processes , 1998 .

[7]  Bilal M. Ayyub,et al.  Structural Reliability Assessment Using Latin Hypercube Sampling , 1990 .

[8]  Reginald DesRoches,et al.  Methodology for the development of analytical fragility curves for retrofitted bridges , 2008 .

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

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

[11]  Jamie E. Padgett,et al.  Probabilistic seismic loss assessment of aging bridges using a component‐level cost estimation approach , 2011 .

[12]  Reginald DesRoches,et al.  Bridge Functionality Relationships for Improved Seismic Risk Assessment of Transportation Networks , 2007 .

[13]  C. Allin Cornell,et al.  Probabilistic seismic demand analysis of nonlinear structures , 1999 .

[14]  Jamie E. Padgett,et al.  Seismic Vulnerability Assessment of Retrofitted Bridges Using Probabilistic Methods , 2007 .

[15]  Bozidar Stojadinovic,et al.  Probabilistic seismic demand model for curved reinforced concrete bridges , 2012, Bulletin of Earthquake Engineering.

[16]  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 .

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