Version 1
: Received: 16 December 2023 / Approved: 18 December 2023 / Online: 18 December 2023 (13:56:46 CET)
Version 2
: Received: 18 December 2023 / Approved: 20 December 2023 / Online: 20 December 2023 (09:06:14 CET)
Ferranti, G.; Greco, A.; Pluchino, A.; Rapisarda, A.; Scibilia, A. Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques. Buildings2024, 14, 309.
Ferranti, G.; Greco, A.; Pluchino, A.; Rapisarda, A.; Scibilia, A. Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques. Buildings 2024, 14, 309.
Ferranti, G.; Greco, A.; Pluchino, A.; Rapisarda, A.; Scibilia, A. Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques. Buildings2024, 14, 309.
Ferranti, G.; Greco, A.; Pluchino, A.; Rapisarda, A.; Scibilia, A. Seismic Vulnerability Assessment at an Urban Scale by Means of Machine Learning Techniques. Buildings 2024, 14, 309.
Abstract
Seismic vulnerability assessment in urban areas would in principle require the detailed modeling of each single building and the implementation of complex numerical calculations. This procedure is clearly difficult to apply at urban scale where many buildings have to be considered and therefore it is essential to have simplified, but at the same time reliable, approaches to vulnerability assessment. Among the proposed strategies, one of the most interesting concerns the application of machine learning algorithms, able to classify buildings according to their vulnerability on the basis of training procedures applied to existing datasets. In this paper machine learning algorithms will be applied to a dataset which collects and catalogs the structural characteristics of a large number of buildings and reports the damage observed in L’Aquila territory during the intense seismic activity occurred in 2009. A combination of a trained neural network and a random forest algorithm allows to identify an opportune “a-posteriori” vulnerability score, deduced from the observed damage, which is compared to an “a-priori” vulnerability one, evaluated taking into account characteristic indexes for building’s typologies. By means of this comparison an inverse approach to seismic vulnerability assessment, which can be extended to different urban centers, is proposed.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
