Machine learning-based identification of vulnerability factors for masonry buildings in aggregate: The historicalcentre of casentino hit by the 2009 l'aquila earthquake

Silvia Pinasco, Sergio Lagomarsino, Caterina Carocci, Andrea Coraddu, Luca Oneto, Serena Cattari

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

32 Downloads (Pure)

Abstract

Seismic events in Italy and worldwide have highlighted the high vulnerability of unreinforced masonry (URM) structures in small historical centres. A key feature of these settlements is to be mostly composed of buildings in aggregate, i.e., interconnected by a more or less structurally effective connection. The seismic assessment of such buildings is quite debated in the literature and no shared tools procedures are currently available. The difficulty of standardization derives from the fact that structural units can be characterized by multiple features and configurations that determine a vast number of vulnerability factors, whose interdependency is not straightforward to be identified. The paper addresses this issue by combining evidence-based damage data with the potential offered by Machine Learning (ML) technique. Real data are used in combination with state-of-the-art ML algorithms carefully tuned via an advanced statistical procedure for two main purposes. The first one will be able to predict possible URM damages based on the vulnerability factor in both interpolation and extrapolation scenarios. The second purpose of the ML-based techniques will be to predict the most important vulnerability factors in making these predictions, namely to make the ML-based model explainable and informative about the underlying phenomena and not just predictive. The small historic centre of Casentino, hit by the 2009 L'Aquila earthquake, is adopted in the paper as the first test case study. A large amount of data was collected after the earthquake through in-situ surveys made by the Universities of Genova, Catania and Rome. Data include both geometric and structural factors, i.e., the input data supplied to the ML algorithm, as well as the actual seismic damage mechanisms, i.e., the output data expected to be predicted by the ML algorithm. As first application, ML techniques are applied only to data acquired on out-of-plane mechanisms.

Original languageEnglish
Title of host publicationProceedings of the 9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering
Subtitle of host publicationCOMPDYN 2023
EditorsM. Papadrakakis, M. Fragiadakis
PublisherEccomas Proceedia
Pages1236-1248
DOIs
Publication statusPublished - 2023
Event9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2023 - Athens, Greece
Duration: 12 Jun 202314 Jun 2023

Publication series

NameCOMPDYN Proceedings
ISSN (Print)2623-3347

Conference

Conference9th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN 2023
Country/TerritoryGreece
CityAthens
Period12/06/2314/06/23

Bibliographical note

The study presented in the paper was developed within the research activities carried out in the frame of 2022-2024 ReLUIS Project – WP10 Masonry Structures (Coordinator - Prof. Guido Magenes). This project has been funded by the Italian Department of Civil Protection. Note that the opinions and conclusions presented by the authors do not necessarily reflect those of the funding entity.

Keywords

  • buildings in aggregate
  • machine learning
  • masonry
  • seismic vulnerability

Fingerprint

Dive into the research topics of 'Machine learning-based identification of vulnerability factors for masonry buildings in aggregate: The historicalcentre of casentino hit by the 2009 l'aquila earthquake'. Together they form a unique fingerprint.

Cite this