07 Feb 2023
 | 07 Feb 2023
Status: this preprint is currently under review for the journal NHESS.

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

Abstract. Assessing or forecasting seismic damage to buildings is an essential issue for earthquake disaster management. In this study, we explore the efficacy of several machine learning models for damage characterization, trained and tested on the database of damage observed after Italian earthquakes (DaDO). Six regression- and classification-based machine learning models were considered: random forest, gradient boosting and extreme gradient boosting. The structural features considered were divided into two groups: all structural features provided by DaDO or only those considered to be the most reliable and easiest to collect (age, number of storeys, floor area, building height). Macroseismic intensity was also included as an input feature. The seismic damage per building was determined according to the EMS-98 scale observed after seven significant earthquakes occurring in several Italian regions. The results showed that extreme gradient boosting classification is statistically the most efficient method, particularly when considering the basic structural features and grouping the damage according to the traffic-light based system used, for example, during the post-disaster period (green, yellow and red). The results obtained by the machine learning-based heuristic model for damage assessment are of the same order of accuracy as those obtained by the traditional Risk-UE method. Finally, the machine learning analysis found that the importance of structural features with respect to damage was conditioned by the level of damage considered.

Subash Ghimire et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-7', Zoran Stojadinovic, 26 Feb 2023
  • RC2: 'Comment on nhess-2023-7', Marta Faravelli, 28 Feb 2023
  • RC3: 'Comment on nhess-2023-7', Petros Kalakonas, 20 Mar 2023

Subash Ghimire et al.

Subash Ghimire et al.


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Short summary
This study looks at using machine learning models to predict damage to buildings during earthquakes. The models were trained using data from earthquakes in Italy and results showed that a model can result good estimate of damage. The accuracy of the machine learning-based model was similar to that of a traditional method used for predicting damage. This method can be used for rapid estimate of damage during emergency situations.