Investigation of seismic damage to existing buildings by using remotely observed images

Abstract

A prolonged and highly destructive seismic sequence struck Central Italy, spanning from August 24, 2016, to January 2017. This series of earthquakes resulted in widespread structural failures and extensive damage across four regions along the Apennines: Lazio, Umbria, Marche, and Abruzzo. In the context of this study, based on post-earthquake remotely observed damages data, we conducted an in-depth empirical examination of the vulnerability of various types of buildings, including reinforced concrete, masonry, and steel structures. These valuable data, accessible through a Google Street View application, furnished critical insights into building locations, characteristics, and spatially observed patterns of damage before and after the seismic events. We undertook a meticulous review and comparison of this extensive dataset to ensure its consistency with prior earthquake observations, fragility functions, and relevant scientific literature. Our research involved a comprehensive examination of selected structures by combining post-earthquake inspections and visual assessments using Google Street View. This comprehensive study allowed us to compare similar buildings with and without damage, generating significant statistical insights into the types of structural pathologies that lead to localized and widespread damage, as well as instances of structural collapses or domino effects. Furthermore, we propose a preliminary glimpse at the power of Machine Learning techniques into earthquake risk assessment. We suggest that this approach could enhance the robustness and accuracy of modeling procedures, which are essential when addressing such complex challenges.