Multi-hazard system-level logit fragility functions

Abstract

Fragility functions are used to represent the probability of failure of a structure or lifeline system conditional upon a hazard or set of hazards and are essential in the performance-based design process. Continuous lognormal damage fragilities are traditional, but recent formulations have implemented logit transformations from the family of generalized linear models for categorical data with a binary outcome (e.g., failure, no failure). In wind engineering, single hazard parameters derived from correlated variables (e.g., integrated kinetic energy, IKE) have been employed to indirectly include the effect of more than one hazard variable; however, even with more general hazard metrics, the lognormal formulation is still unduly restrictive for realistic fragility modeling. Using a statistical approach based on a logit formulation, a shift towards more robust fragility functions can be achieved. Because of its simplicity and ability to represent multiple predictor variables to improve the fitted model, this paper proposes use of the logit formulation of the fragility function at the system level for two or more simultaneous weather hazards. Successful applications of the model to characterize lifeline systemlevel fragility functions for electric power delivery during Hurricane Isaac in Louisiana and Hurricane Sandy in New York City using in-situ damage and hazard data are shown. While the results here are empirically derived, the modeling approach may be expanded for other structural systems subject to multiple loadings or demand variables.

Vulnerability and Robustness of Civil Infrastructure Systems to Hurricanes

“…Recent research into renewable power to enhance green infrastructure suggests that the implementation of micro-grids at the community level may also result in a more robust power system overall. Another approach to system robustness is to add power generation redundancy at the individual building level through building integrated photovoltaic panels and wind turbines [e.g., Wang et al (2016)]. Ideally the recovery of the infrastructure systems should be based upon the expectations and perspectives of the community, rather than the infrastructure operators.…”

“…In order to obtain fragility models F(Xmax|H1, …Hn) at the system level for power delivery, weather variable data (H1, …, Hn) at the same geographical scale are necessary. For limited hurricane data sets, fragilities of the following logistic regression format were found, based upon previous work by Reed et al (2016): …”

“…It can be seen that the damping parameter ζ remains in the range of 1.01-2, whereas the frequency parameter ω varies more broadly. In addition to Hurricane Isaac, models were fit to Hurricane Sandy outage data per locality in New York City as discussed in Reed et al (2016). The relationship between VUL parameter and the X0 value is examined in Figure 6 or the three spatial scales.…”

Multihazard Hurricane Fragility Model for Wood Structure Homes Considering Hazard Parameters and Building Attributes Interaction

“…Data-based fragility models have been widely implemented to estimate the probability of collapse or being in or exceeding a specified damage state for buildings subjected to tsunami (e.g., Reese et al, 2007Reese et al, , 2011Koshimura et al, 2009;Suppasri et al, 2012Suppasri et al, , 2013Charvet et al, 2014aCharvet et al, ,b, 2015Muhari et al, 2015) and earthquake (Porter et al, 2007;Tang et al, 2012;Lallemant et al, 2015). Although not specific to building, Padgett et al (2012) empirically modeled damage to coastal bridges along the US Gulf Coast using multivariate logistic regression models, Reed et al (2016) developed a logit fragility model to predict damage for power systems, and Kameshwar and Padgett (2018) developed a wind buckling and storm surge flotation fragility models for oil storage tanks.…”

Fragility Curves for Wide-Flange Steel Columns and Implications for Building-Specific Earthquake-Induced Loss Assessment

“…In a similar manner, Chiozzi and Miranda (2017) related cracking and crushing of masonry infill walls with masonry and mortar compressive strengths in addition to the SDR. Reed et al (2016) developed fragility curves for electric power lifeline systems subjected to multiple weather hazards. Yazdi et al (2016) demonstrated the applicability of multinomial ordinal logistic regression to produce multivariate fragility curves for reinforced concrete shear walls.…”