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Resilient Improvement of Historic Districts via Digital Tools. The Virtualization of Energy Retrofit Actions Using Simplified CityGML-Based Plans

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Abstract

The management of the built environment directly exposed to the effect of climate change and the assessment of buildings for their participation in such environmental processes are part of the main challenges in scientific, technical and administrative fields. On the other hand, the increasing endorsement of IT expertise and tools in traditional processes offers the opportunity to manage specific knowledge and relations using Digital Models. As unusual parts, historic centers represent unconventional districts of cities in treating mitigation and adaptation strategies, because of their overordered system of preservation normative framework and inherent formal and technical variabilities. Historic centers, as well as Cultural Heritage, require specific plans of actions for their resilient improvement to climate change. In that sense, the paper discusses and tests a simplified Digital Model based on the CityGML standard for the creation of a virtual recovery plan of historic districts, starting from previous results in supporting their management in an energy-resilient perspective. The Model has been conceived as a structured system of technical and scientific knowledge, re-organized according to the IT structure of CityGML to support public administration in managing adaptive and mitigative actions and practitioners in choosing compatible technical solutions.

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Acknowledgements

This research is funded under the project “AIM1871082-1” of the AIM (Attraction and International Mobility) Program, financed by the Italian Ministry of Education, University and Research (MUR).

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Correspondence to Elena Cantatore .

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Cantatore, E., Lasorella, M., Fatiguso, F. (2021). Resilient Improvement of Historic Districts via Digital Tools. The Virtualization of Energy Retrofit Actions Using Simplified CityGML-Based Plans. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12956. Springer, Cham. https://doi.org/10.1007/978-3-030-87010-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-87010-2_11

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