Parametric and Machine Learning-Based Analysis of the Seismic Vulnerability of Adobe Historical Buildings Damaged After the September 2017 Mexico Earthquakes

In September 2017, two strong earthquakes hit the central region of Mexico, producing substantial damage to the historical buildings. A retroactive analysis for assessing the pre-event seismic vulnerability of these constructions allowed for testing the suitability of an existing parameter-based approach based on material and geometrical features. More than 160 adobe buildings in four municipalities of the State of Morelos were surveyed and included in a vulnerability-oriented GIS database. Data were collected on-site and managed by resorting to open-source GIS software combined with a Python-based database management tool and a cloud-based platform for onsite data collection using mobile devices. The parameter-based approach was used for assessing the analytical seismic vulnerability of the buildings and implementing a secondary, more conservative assessment that considers uncertainties associated with the data acquisition process. The capabilities of the database were further used to train a Machine Learning algorithm aimed at overcoming some representativeness limitations of the parameter-based analytical method. This third approach was found to be suitable for assessing the vulnerability of the building typologies addressed in this investigation. Although the implementation discussed in this paper is limited to a specific vernacular typology, it can be used to conduct customized local calibrations.

Automated summary

This study assesses the seismic vulnerability of historical buildings in the central region of Mexico using a parameter-based approach. Data on more than 160 adobe buildings were collected and managed using open-source GIS software, Python-based database management tool, and a cloud-based platform for onsite data collection. The study finds that the parameter-based approach is effective and also trains a Machine Learning algorithm using the database to overcome representativeness limitations of the method.