Fragility Function Development of RC Building Portfolio for Use in Earthquake–Tsunami Community Resilience Studies

Natural hazards such as earthquakes and earthquake-triggered tsunamis can pose a more significant threat to some coastal communities than hurricanes and floods. Accordingly, a tsunami event followed by a source earthquake may cause fatalities or injuries and long-term socio-economic losses to coastal communities. Community resilience analysis can be used as an effective means to study and mitigate these potential impacts by modeling the community and its infrastructure under the compound (or successive) risk of earthquakes and tsunamis. For infrastructure damage assessment, the resilience frameworks rely on the fragility functions that are developed for different types of structures within the community of interest. However, current methodologies rely on two separate and independent fragility curve sets, i.e., one for earthquake and the other for tsunami, neglecting the direct cascading cumulative effects of these two hazards for community-level damage estimation. Further, this level of damage establishes the initial conditions for a full multidisciplinary community resilience analysis. The study summarized in this paper aims to generate earthquake–tsunami fragility surfaces for a basic portfolio of reinforced concrete (RC) frames (ductile and nonductile systems) using a fully two-phased probabilistic successive analysis that can simultaneously incorporate the direct combinational impacts of the source earthquake followed by the tsunami. It is revealed that the nonductile systems can collapse at some moderate earthquake intensity levels before the tsunami wave arrives, while the ductile systems can still endure the tsunami wave even when partially damaged. This paper reports the controlling parameters of the two-dimensional (2D) and three-dimensional (3D) fragility functions for the proposed minimal portfolio of RC frames subjected to earthquake and subsequent tsunami. Ultimately, these fragility surfaces can be utilized in community-level models to support resilience-informed mitigation decision making.