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 Sci nceDire t Available online at www.sciencedirect.com ScienceDirect

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

Procedia Structural Integrity 44 (2023) 1972–1979

XIX ANIDIS Conference, Seismic Engineering in Italy Improving building inventory with a machine learning approach: application in southern Italy GabriellaTocchi a,* , Maria Polese a , Andrea Prota a XIX ANIDIS Conference, Seismic Engineering in Italy Improving building inventory with a machine learning approach: application in southern Italy GabriellaTocchi a,* , Maria Polese a , Andrea Prota a

a Department of Structures for Engineering and Architecture, University of Naples Federico II Via Claudio 21, 80125, Naples, Italy a Department of Structures for Engineering and Architecture, University of Naples Federico II Via Claudio 21, 80125, Naples, Italy

© 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. Abstract The compiling of large-scale building inventory is a fundamental step in the evaluation of seismic risk in a region of interest. For classification of buildings in vulnerability classes, different levels of detail could be employed depending on the model adopted for the vulnerability assessment. Generally, the key structural characteristics of exposed building needed for the application of more refined vulnerability models are not easily available. Aimed to improve the information quality relevant to exposure, the interview-based form Cartis was recently implemented by ReLUIS under supervision of Italian Civil Protection Department within “Territorial Themes” DPC-ReLUIS project. Being based on an interview protocol, this form allows to rapidly detect relevant buildings data at urban level. Despite Cartis is an extremely useful tool for the compiling of regional scale inventories, nowadays the database covers only the 5% of Italian territory. This study aims to exploit the actual data available in Cartis database to build an exposure model that could be applied for preliminary inventory evaluation also in towns where the Cartis form is not compiled yet. To this end, a machine learning approach is applied. Cartis data available for several municipalities in Campania region are used together with basic information on buildings and population provided by Italian census data (ISTAT) to train a supervised learning algorithm for identification of homogenous urban sectors in terms of buildings features (i.e., Town Compartments) in municipal area. Once the algorithm is implemented, it may be employed in other municipalities to delimit relative Town Compartments and to identify most common building typologies within them. © 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: Cartis; Exposure modelling; Machine learning; Abstract The compiling of large-scale building inventory is a fundamental step in the evaluation of seismic risk in a region of interest. For classificat o of buildings in v lnerab lity classe , different leve of detail could be employed dependi g on the model adopted for the vulnerability asse sment. Generally, the key structural charact ristics of exposed buil ing needed for the applic ti n of more refined vulnerability odels ar not easily available. Aimed to improve th information quality relevan to ex osure, the intervi w-based fo m Cartis was rec tly implemented by ReLUIS under superv si n of Italian Civil Protecti n Depa tment within “Territorial Themes” DPC-ReLUIS proj ct. Being based on a intervi w pr tocol, th s form allows to rapidly det c relevant buildings data at urban level. Despite Car is is an extr mely useful tool for the mpil ng f regi nal scale inventori s, nowadays the datab se covers only the 5% of Italian territo y. This tudy aims to expl it the actual data available in Cartis database to build n exposure model that could be appli d f preliminary inventory evaluation lso in towns where the form i not compiled yet. To this end, a machine l rning app oach is applied. Cartis dat available for several municipalities n Ca pania region are used t gether with b sic information on buildings and population provided by Italian census data (ISTAT) to train a supervised learning algo ithm for identification of homoge ous urban sec ors in terms of build ngs features (i.e., own Compartments) n municipal rea. Once the algorithm is implemented, it may be employed in other municipalities to delimit relative Town Compartments nd to identify most com on bui ding typologies within them. © 2022 he 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: Cartis; Exposure modelling; Machin learning;

* Corresponding author. Tel.: +39-0817683485 E-mail address: gabriella.tocchi@unina.it * Corresponding author. Tel.: +39-0817683485 E-mail address: gabriella.tocchi@unina.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.2023.01.252