dc.contributor.author | Carta, Silvio | |
dc.contributor.author | Turchi, Tommaso | |
dc.contributor.author | Pintacuda, Luigi | |
dc.date.accessioned | 2022-06-08T11:15:01Z | |
dc.date.available | 2022-06-08T11:15:01Z | |
dc.date.issued | 2022-04-15 | |
dc.identifier.citation | Carta , S , Turchi , T & Pintacuda , L 2022 , MEASURING RESILIENT COMMUNITIES : An analytical and predictive tool . in CAADRIA 2022 Proceedings : 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia / Sydney, Australia . CAADRIA2022 , Sydney , Australia , 9/04/22 . < https://caadria2022.org/wp-content/uploads/2022/04/68-1.pdf > | |
dc.identifier.citation | conference | |
dc.identifier.other | ORCID: /0000-0002-7586-3121/work/123559530 | |
dc.identifier.uri | http://hdl.handle.net/2299/25550 | |
dc.description | © 2022 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong. This is the accepted manuscript version of an article which has been published in final form at https://caadria2022.org/measuring_resilient_communities_an_analytical_and_predictive_tool/ | |
dc.description.abstract | This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo. | en |
dc.format.extent | 10 | |
dc.format.extent | 1251188 | |
dc.language.iso | eng | |
dc.relation.ispartof | CAADRIA 2022 Proceedings | |
dc.title | MEASURING RESILIENT COMMUNITIES : An analytical and predictive tool | en |
dc.contributor.institution | Art and Design | |
dc.contributor.institution | Design Research Group | |
dc.contributor.institution | Zero Carbon Lab | |
dc.contributor.institution | Centre for Climate Change Research (C3R) | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | School of Creative Arts | |
dc.identifier.url | https://caadria2022.org/wp-content/uploads/2022/04/68-1.pdf | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |