Intensity and damage statistics of the September 19,2017 Mexico earthquake: Influence of soft story and corner asymmetry on the damage reported during the earthquake

Abstract

A destructive intraslab earthquake occurred in Mexico City on September 19, 2017 (Mw 7.1), causing significant damage and hundreds of human losses not only in the epicentral area, but also in the States of Morelos, Puebla, Mexico and in Mexico City. Only in Mexico City itself, around 230 people died, and more than 40 buildings collapsed. The intensities recorded in some lakebed areas of the city, especially in zones with soil periods around 1.5 s, were relatively high, even surpassing spectral values of 1.0 g; the vertical component, due to the proximity of the earthquake, was unusually high for Mexico City. The 2017 earthquake raised questions critical to understanding the city’s seismic vulnerability and resilience, and they are partly answered in this article. Using 77 accelerometric stations, the amplification pattern of the seismic intensities is characterized as well as the correlations of buildings structural characteristics with the site effects. A comprehensive statistical analysis of the damages is shown to analyze and understand the structural behavior of damaged buildings. It is including not only the structural types and the year of construction, but also the main structural problems identified (structural pathologies), such as irregularities, both in elevation and plan, soft story, and corner effect. The building damage database was constructed with 2125 reports of buildings carried out by universities and engineering associations after the earthquake, of which 543 had severe damage. It is also included the information of all buildings with no damage in the city thanks to the cadastral information provided by the Mexico City government, and post-earthquake inspections and visual inspections using Google Street View. A full study of selected neighborhoods, which compares similar buildings with and without damage, is included, yielding relevant statistical information on which pathologies cause more damage and even collapses.