PSI - Issue 44

ScienceDirect Structural Integrity Procedia 00 (2022) 000–000 Structural Integrity Procedia 00 (2022) 000–000 Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceD rect Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

Procedia Structural Integrity 44 (2023) 1980–1987

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. In this way, meaningful parameters that influence seismic vulnerability can be acquired remotely from images, with a significant reduction in time and costs. This can also lead to a better vulnerability assessment, thus to more precise seismic risk estimates. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy Keywords: seismic risk assessment; Italian residential buildings; satellite images; street view; machine learning; convolutional neural networks. Abstract Seismic risk assessment represents a major challenge in countries with considerable seismic hazard and vulnerable built heritage, such as Italy. Especially for large-scale analys s, vulnerability evaluation may r ult in very time-consuming and expensive investigations. In order to tackle thi problem, atellite im ges and stree view pictures can b used to auto atically collect data about buildi gs through Convo u ional Neural Ne works (CNNs). In this way, meaningful parameters that influenc seismic vulnerability can be acquired remotely from images, with a significant reduction in time a d costs. This can also lead to a better vulnerabil ty assessment, thus to r precise seismic risk estimates. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license ( https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review u der re ponsibility of scientific committe of the XIX ANIDIS C nfere ce, Seismic Engineering in Italy K ywords: seismic risk assessment; Italian reside tial buildings; satellite images; street view; machi e learning; convolutional neural networks. XIX ANIDIS Conference, Seismic Engineering in Italy Automatic identification of residential building features using machine learning techniques Carpanese Pietro a *, Donà Marco a , da Porto Francesca a a Deparment of Geosciences, University of Padova, Via Giovanni Gradenigo 6, Padova 35131, Italy Abstract Seismic risk assessment represents a major challenge in countries with considerable seismic hazard and vulnerable built heritage, such as Italy. Especially for large-scale analyses, vulnerability evaluation may result in very time-consuming and expensive investigations. In order to tackle this problem, satellite images and street view pictures can be used to automatically collect data about buildings through Convolutional Neural Networks (CNNs). XIX ANIDIS Conference, Seismic Engineering in Italy Automatic identification of residential building features using machine learning techniques Carpanese Pietro a *, Donà Marco a , da Porto Francesca a a Deparment of Geosciences, University of Padova, Via Giovanni Gradenigo 6, Padova 35131, Italy

* Corresponding author. Tel.: +39 0498275560. E-mail address: pietro.carpanese@phd.unipd.it * Corresponding author. Tel.: +39 0498275560. E-mail address: pietro.carpanese@phd.unipd.it

2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy 2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. 10.1016/j.prostr.2023.01.253