MEASURING RESILIENT COMMUNITIES: An analytical and predictive tool

Silvio Carta, Tommaso Turchi, Luigi Pintacuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationCAADRIA 2022 Proceedings
Subtitle of host publication27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia / Sydney, Australia
Number of pages10
Publication statusPublished - 15 Apr 2022
EventCAADRIA2022 - Sydney, Australia
Duration: 9 Apr 202215 Apr 2022


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