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

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Revised manuscript accepted for NHESS
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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. 
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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.
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