Articles | Volume 23, issue 10
https://doi.org/10.5194/nhess-23-3199-2023
https://doi.org/10.5194/nhess-23-3199-2023
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

Related authors

Towards improving the spatial testability of aftershock forecast models
Asim M. Khawaja, Behnam Maleki Asayesh, Sebastian Hainzl, and Danijel Schorlemmer
Nat. Hazards Earth Syst. Sci., 23, 2683–2696, https://doi.org/10.5194/nhess-23-2683-2023,https://doi.org/10.5194/nhess-23-2683-2023, 2023
Short summary
California earthquake insurance unpopularity: the issue is the price, not the risk perception
Adrien Pothon, Philippe Gueguen, Sylvain Buisine, and Pierre-Yves Bard
Nat. Hazards Earth Syst. Sci., 19, 1909–1924, https://doi.org/10.5194/nhess-19-1909-2019,https://doi.org/10.5194/nhess-19-1909-2019, 2019
Short summary

Related subject area

Earthquake Hazards
Risk-informed representative earthquake scenarios for Valparaíso and Viña del Mar, Chile
Hugo Rosero-Velásquez, Mauricio Monsalve, Juan Camilo Gómez Zapata, Elisa Ferrario, Alan Poulos, Juan Carlos de la Llera, and Daniel Straub
Nat. Hazards Earth Syst. Sci., 24, 2667–2687, https://doi.org/10.5194/nhess-24-2667-2024,https://doi.org/10.5194/nhess-24-2667-2024, 2024
Short summary
Harmonizing seismicity information in Central Asian countries: earthquake catalogue and active faults
Valerio Poggi, Stefano Parolai, Natalya Silacheva, Anatoly Ischuk, Kanatbek Abdrakhmatov, Zainalobudin Kobuliev, Vakhitkhan Ismailov, Roman Ibragimov, Japar Karaev, Paola Ceresa, and Paolo Bazzurro
Nat. Hazards Earth Syst. Sci., 24, 2597–2613, https://doi.org/10.5194/nhess-24-2597-2024,https://doi.org/10.5194/nhess-24-2597-2024, 2024
Short summary
Comparing components for seismic risk modelling using data from the 2019 Le Teil (France) earthquake
Konstantinos Trevlopoulos, Pierre Gehl, Caterina Negulescu, Helen Crowley, and Laurentiu Danciu
Nat. Hazards Earth Syst. Sci., 24, 2383–2401, https://doi.org/10.5194/nhess-24-2383-2024,https://doi.org/10.5194/nhess-24-2383-2024, 2024
Short summary
Modelling seismic ground motion and its uncertainty in different tectonic contexts: challenges and application to the 2020 European Seismic Hazard Model (ESHM20)
Graeme Weatherill, Sreeram Reddy Kotha, Laurentiu Danciu, Susana Vilanova, and Fabrice Cotton
Nat. Hazards Earth Syst. Sci., 24, 1795–1834, https://doi.org/10.5194/nhess-24-1795-2024,https://doi.org/10.5194/nhess-24-1795-2024, 2024
Short summary
Scoring and ranking probabilistic seismic hazard models: an application based on macroseismic intensity data
Vera D'Amico, Francesco Visini, Andrea Rovida, Warner Marzocchi, and Carlo Meletti
Nat. Hazards Earth Syst. Sci., 24, 1401–1413, https://doi.org/10.5194/nhess-24-1401-2024,https://doi.org/10.5194/nhess-24-1401-2024, 2024
Short summary

Cited articles

ATC: ATC-20-1, Field Manual: Postearthquake Safety Evaluation of Buildings Second Edition, Applied Technology Council, Redwood City, California, ISBN ATC20-1, 2005. 
Azimi, M., Eslamlou, A. D., and Pekcan, G.: Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review, Sensors, 20, 2778, https://doi.org/10.3390/s20102778, 2020. 
Baggio, C., Bernardini, A., Colozza, R., Pinto, A. V., and Taucer, F.: Field Manual for post-earthquake damage and safety assessment and short term countermeasures (AeDES) Translation from Italian: Maria ROTA and Agostino GORETTI, European Commission – Joint Research Centre – Institute for the Protection and Security of the Citizen, EUR 22868, 2007. 
Bazzurro, P., Cornell, C. A., Menun, C., and Motahari, M.: Guidelines for seismic assessment of damaged buildings, in: 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada, 74–76, https://doi.org/10.5459/bnzsee.38.1.41-49, 2004. 
Branco, P., Ribeiro, R. P., Torgo, L., Krawczyk, B., and Moniz, N.: SMOGN: a Pre-processing Approach for Imbalanced Regression, Proc. Mach. Learn. Res., 74, 36–50, 2017. 
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.
Altmetrics
Final-revised paper
Preprint