This paper summarizes analyses of structural damage to reinforced concrete buildings seen after the 2017 Mexico City earthquake. With respect to the 2017 earthquake, the authors are part of a multi-institution National Science Foundation (NSF) RAPID effort that involved several in-field data collection missions yielding a dataset with detailed metadata for nearly 120 buildings. This data has been analyzed in conjunction with a high-density data set of around 1400 buildings from the National Autonomous University of Mexico (UNAM). The focus of analyses includes identifying correlations between building attributes (age, height, column ratio, design vulnerabilities, etc.) and site attributes (soil zone and local seismicity) to observed damage severity.

The presented analyses rely heavily on geo-spatial mapping of data sets as Mexico City, constructed on a lakebed and having a unique soil profile and variance in seismicity, has geographically variable severity of reinforced concrete building damage. Specifically, the research team has leveraged the capabilities of geo-spatial mapping software ArcGIS Pro to investigate damages with respect to geotechnical zones and the PGA and PSA values from an array of ground motion stations active in the region during the 2017 earthquake. Thus, it has been possible to link metadata for geotechnical zone and ground motion for the closest station to each building.

Aside from conclusions about the 2017 Mexico earthquake related to structural damage in reinforced concrete buildings, the authors will share the workflow for data curation and visualization methods for both ArcGIS Pro and MATLAB. These enable raw data from buildings, ground motion stations, and soil zone maps to be rapidly transformed into meaningful data structures, quantitative graphs, and geo-spatial maps. These protocols can be extremely powerful in the aftermath of a subsequent major earthquake to post-process and analyze reconnaissance data in a manner that provides the necessary evidence to support changes in current seismic design codes of practice.


Architectural Engineering

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URL: https://digitalcommons.calpoly.edu/aen_fac/142