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

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 (the Database of Observed Damage – DaDO). Six models were considered: regression- and classification-based machine learning models, each using 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), 68 % of buildings were correctly classified. The results obtained by the machine-learning-based heuristic model for damage assessment are of the same order of accuracy (error values were less than 17 %) 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.