Damage Assessment on Buildings Following the 19th September 2017 Puebla, Mexico Earthquake

• Main Authors: Samuel Roeslin, Quincy T. M. Ma, Hugón Juárez García
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-12-01
• Series: Frontiers in Built Environment
• Subjects: seismic damage assessment; 2017 Puebla earthquake; building damage; seismic assessment forms; Global Earthquake Model (GEM); European Macroseismic Scale EMS-98
• Online Access: https://www.frontiersin.org/article/10.3389/fbuil.2018.00072/full

Understanding seismic risk enables efficient resource allocation in the effort to increase the resilience of our cities and communities. Field reconnaissance and data collection following disasters document the damaging effects of earthquakes to enable lessons and wisdom to accumulate from past events. An important aim of such field data analysis is establishing a better understanding of building performance such as causes of building failures. These lessons provide essential basis to improve our design codes, develop regulations and policies, to increase building resilience by addressing the infrastructure vulnerability. Currently, to make use of the datasets from around the world, significant effort is required to decode the data which often have unique local and regional context and bias. The struggle begins at data collection where there is a lack of consistent methodology and definitions that can adequately cover the regional nuance. This manuscript proposes a new paper-based tool which addresses the need for a global yet detailed universal methodology for building damage assessment post-earthquakes. The new form is based on the GEM taxonomy v2.0 and the European Macroseismic Scale EMS-98. The recent Mexican earthquake from the 19 September 2017 led to significant building damage in the capital Mexico City and the state of Morelos. A team from New Zealand assessed damage throughout the capital and tested the new paper form in Calle La Morena. The street case study presents a novel visualization of the damage data and buildings characteristics which highlights the correlation between the damage and the building features. It is hoped that this kind of illustration will lead to better comprehension of the damage drivers.