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Alive; Making buildings more intelligent for a sustainable future

Amirsalar Mazaherkermani

Alive; Making buildings more intelligent for a sustainable future.

Rel. Lorenzo Savio. Politecnico di Torino, Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile, 2022

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Abstract:

Buildings are responsible for more than a third of the global carbon emission, and this number will increase unless there are some significant breakthroughs in the way they are built and managed. In addition, most sustainable buildings are also not performing as well as expected. For such reasons, it is crucial to make buildings more intelligent from their construction to their end of life. The number of factors affecting a building efficiency, sustainability, and performance is so numerous that it is essentially impossible to consider all of them during the design process and even if it is achievable to optimize the design with the most accurate assumptions; over the lifespan of the building, many of these factors might alter or act unpredictably. This project aims to demonstrate different factors that a building should follow to be considered intelligent. It focuses on an intelligent algorithm that reacts to real-time activities and behaviors. This algorithm provides the foundation for future adaptations in response to new needs and functions. Such an algorithm could be easily applied to existing buildings and increase their performance by offering a real-time management method of the resources and systems. This is necessary considering that many relatively new buildings are not performing efficiently worldwide. Although this algorithm can increase the performance of the building and help them to adapt to the real-time condition, other design factors are still essential to achieve a fully sustainable building, such as minimizing the material use, simplifying the construction, and designing for disassembly. In the end, The only way to achieve higher sustainability in buildings is to utilize intelligent management systems, AI, and behavior prediction to minimize human errors, the negative impact of unexpected events, and the unpredicted behavior of users.

Relatori: Lorenzo Savio
Anno accademico: 2021/22
Tipo di pubblicazione: Elettronica
Numero di pagine: 77
Soggetti:
Corso di laurea: Corso di laurea magistrale in Architettura Per Il Progetto Sostenibile
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-04 - ARCHITETTURA E INGEGNERIA EDILE-ARCHITETTURA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/21894
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