Parametric and machine learning-based analysis of the seismic vulnerability of adobe historical buildings damaged after the September 2017 Mexico earthquakes

Ramírez Eudave, Rafael; Ferreira, Tiago Miguel; Vicente, Romeu; Lourenco, Paulo B.; Peña, Fernando

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Apr 3, 2023
• Online Publication Date: Apr 16, 2023
• Publication Date: 2024
• Deposit Date: May 2, 2023
• Publicly Available Date: May 23, 2024
• Journal: International Journal of Architectural Heritage
• Print ISSN: 1558-3058
• Electronic ISSN: 1558-3066
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 6
• Pages: 940-963
• DOI: https://doi.org/10.1080/15583058.2023.2200739
• Keywords: Geographical Information System; adobe; damage database; field survey; machine learning; seismic damages; seismic vulnerability assessment
• Public URL: https://uwe-repository.worktribe.com/output/10723657
• Publisher URL: https://www.tandfonline.com/doi/full/10.1080/15583058.2023.2200739