Empirical seismic fragility models for Nepalese school buildings

Abstract

Empirical vulnerability models are fundamental tools to assess the impact of future earthquakes on urban settlements and communities. Generally, they consist of sets of fragility curves that are derived from georeferenced post-earthquake damage data. Following the 2015 Nepal earthquake sequence, the World Bank, through the Global Program for Safer Schools, conducted a Structural Integrity and Damage Assessment (SIDA) of about 18,000 school buildings in the earthquake-affected area. In this work, the database is utilized to identify the main structural characteristics of the Nepalese school building stock. For the first time, extended SIDA school damage data is processed to derive fragility curves for the main structural typologies. Data sets for each structural typology are used for a Bayesian updating of existing fragilities to obtain regional models for Nepalese schools. These fragility estimates can be adopted to assess potential seismic losses of the school infrastructure in Nepal. Additionally, they can be used for calibrating loss assessment studies in the wider Himalayan region where the structural typologies are similar.

FAST-NEPAL: Regionally Calibrated Spectral Method for Reinforced Concrete With Masonry Infills

“…Fragility curves for DS1 are not given. Giordano et al (2020b) gives a η value of 0.19 g for DS2 compared with the aggregated data for Ward-35, which gives a value of 0.49 g (see Figure 13D). This discrepancy is due to a lower quality of building control found in more rural parts of Nepal from which most of the data from Giordano et al are located.…”

“…This is likely caused by different assumptions for the Ground Motion Prediction Equations used for the different studies and social and political considerations about seismic design implementation in the country to improve resilience and best practice. Giordano et al (2020b) used data from the Structural Integrity and Damage Assessment of approximately 18,000 school buildings across Nepal, assembled by the World Bank, to derive empirical fragilities. These fragilities are separated on the basis of structural typology and carried out using a Bayesian updating procedure.…”

Learning From Success, Not Catastrophe: Using Counterfactual Analysis to Highlight Successful Disaster Risk Reduction Interventions

“…The curves were generated using a Bayesian approach to incorporate wellestablished fragility models such as the HAZUS database (Federal Emergency Management Agency, 2015) and World Bank's Structural Integrity and Damage Assessment database (SIDA) that was conducted under the Global Program for Safer Schools (Worldbank, 2019). The collapse fragility curves from Giordano et al (2021a) were assigned to the buildings in the OpenDRI dataset based on their structure type -unreinforced load-bearing wall schools were assigned the URM collapse fragility curve, while reinforced concrete schools were assigned the RC collapse fragility.…”

Order Matters: The Benefits of Ordinal Fragility Curves for Damage and Loss Estimation

“…In Method 2, the $$\beta$$ and cutoff values are adjusted so that all damage grades share the same $$\beta$$ value and the adjusted curve meets the original curves at a 10% failure probability. While these post hoc strategies fix the crossing curves and have been used in practice (see, e.g., Giordano et al., 2021; Thapa, Shrestha, Lamichhane, Adhikari, & Gautam, 2020), the ordinal regression provides a solution to this issue with a stronger theoretical grounding.…”