Latest publications

Agent-based model for post-earthquake housing recovery

Rodrigo Costa, Terje Haukaas, and Stephanie E Chang

A framework of agent-based models for housing recovery is presented and used to investigate post-earthquake recovery in the City of Vancouver, Canada. Housing recovery is modeled for a portfolio of buildings, contrasting with the practice of assessing the reconstruction of buildings in isolation. Thus, the presented approach better captures the effect of competition for resources, infrastructure disruptions, and socioeconomic factors on recovery. The analyses include models for damage, inspection, financing, power infrastructure, and labor/materials for repairs. The presented approach is applied to simulate the recovery of 114,832 residential buildings in 22 neighborhoods in Vancouver. Results indicate that recovery after a strong earthquake will take more than three years. The density of old and rented buildings, and the income and immigration status of the homeowners are shown to be good predictors of the speed of recovery for a neighborhood. Mitigation measures are compared and it is shown that retrofitting the most physically vulnerable buildings or doubling the available workforce are effective at reducing housing recovery times. It is demonstrated that the equity in recovery between low and high socioeconomic status homeowners is improved if mitigation measures are implemented. The results presented in this article can inform disaster recovery plans and mitigation actions in Vancouver and similar communities.

Costa, R., Haukaas, T., & Chang, S. E. (2020). Agent-based model for post-earthquake housing recovery. Earthquake Spectra. https://doi.org/10.1177/8755293020944175

Overview of collapsed buildings in Mexico City after the 19 September 2017 (Mw7.1) earthquake

Francisco A. Galvis, Eduardo Miranda, Pablo Heresi, Hector Davalos, & Jorge Ruiz-Garcia

An intraslab normal-faulting earthquake struck the central region of Mexico on 19 September 2017, leading to the collapse of 44 buildings in Mexico City. After the earthquake, the authors collected information in situ and through social media about the collapsed buildings, which was statistically processed to identify the causes of their collapse. This article presents the main collapse statistics, which revealed that 64% of the collapsed buildings had between 1 and 5 stories, 61% had a seismic-force-resisting system based on reinforced concrete columns with flat slabs, 57% experienced a soft-story mechanism, 91% were built before 1985, 43% were located at the corner blocks, and 10% exhibited pounding with neighboring buildings. The spatial distribution of the collapsed buildings and the recorded ground motion features suggest that short- and medium-period buildings having well-known vulnerabilities were particularly prone to collapse under amplified high-frequency seismic waves typical of intraslab normal-faulting earthquakes, such as the 2017 Puebla–Morelos earthquake.

https://journals.sagepub.com/doi/full/10.1177/8755293020936694

G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage

Sabine Loos, David Lallemant, Jack Baker, Jamie McCaughey, Sang-Ho Yun, Nama Budhathoki, Feroz Khan, & Ritika Singh

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially-distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets–and downweight uninformative sources–reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering
forecast.

https://journals.sagepub.com/doi/full/10.1177/8755293020926190

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