Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 10
Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3199-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-3199-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 10
Research article
 | 
05 Oct 2023
Research article |  | 05 Oct 2023

Testing machine learning models for heuristic building damage assessment applied to the Italian Database of Observed Damage (DaDO)

Subash Ghimire, Philippe Guéguen, Adrien Pothon, and Danijel Schorlemmer

Viewed

Total article views: 2,843 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,926 817 100 2,843 110 124
  • HTML: 1,926
  • PDF: 817
  • XML: 100
  • Total: 2,843
  • BibTeX: 110
  • EndNote: 124
Views and downloads (calculated since 07 Feb 2023)
Cumulative views and downloads (calculated since 07 Feb 2023)

Viewed (geographical distribution)

Total article views: 2,843 (including HTML, PDF, and XML) Thereof 2,766 with geography defined and 77 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
Short summary
This study explores the efficacy of several machine learning models for damage characterization, trained and tested on the Database of Observed Damage (DaDO) for Italian earthquakes. Reasonable damage prediction effectiveness (68 % accuracy) is observed, particularly when considering basic structural features and grouping the damage according to the traffic-light-based system used during the post-disaster period (green, yellow, and red), showing higher relevancy for rapid damage prediction.
Read more
Share
Special issue
Harmonized seismic hazard and risk assessment for Europe
Altmetrics
Final-revised paper
Preprint