CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id sigradi2018_1869
id sigradi2018_1869
authors Borda Almeida da Silva, Adriane; dos Santos Nunes, Cristiane; Curth Goulart, Stefani; Harter Silva, Bethina
year 2018
title Impressions of a touristic route: between the null-dimensional and the three-dimensional
source SIGraDi 2018 [Proceedings of the 22nd Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Brazil, São Carlos 7 - 9 November 2018, pp. 638-643
summary This paper reports the experience of a public university digital manufacturing laboratory in producing tactile models to support a tourist route in a historic center. The report includes the reflection on the social and formative, cultural and professional meaning attributed to this production. For this, it uses the theory of the climbing of abstraction, by Vilém Flusser, problematizing the dimensional logic of the media used. This is the representation of the architectural set of the surroundings of a square. Architecture students were involved in the production of the models which were validated by visually impaired individuals.
keywords Tactile models; Universal design; Digital manufacturing; Architectural heritage; Tourist route
series SIGraDi
last changed 2019/05/20 09:14

_id ecaade2020_404
id ecaade2020_404
authors Singh, Manav Mahan, Schneider-Marin, Patricia, Harter, Hannes, Lang, Werner and Geyer, Philipp
year 2020
title Applying Deep Learning and Databases for Energy-efficient Architectural Design - Abstract
source Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 79-87
summary The reduction of energy consumption of buildings requires consideration in early design phases. However, modelling and computation time required for dynamic energy simulations makes them inappropriate in the early phases. This paper presents a performance prediction approach for these phases that is embedded in a multi-level-of-development modelling approach. First, parametric pre-trained modular deep learning components are embedded in the building elements. The energy performance is predicted by composing these components. Second, embodied energy assessment is performed by extracting the information from a database. A calculation module queries the database and calculates the embodied energy. Both, embodied and operational, energy are assembled to predict lifecycle energy demand. The method has been implemented prototypically in a digital modelling environment Revit. A case study serves to demonstrate the application process, the user interaction and the information flows. It shows energy prediction in early design phases to enhance the environmental performance of the building.
keywords BIM; Operational Energy; Embodied Energy; Life-cycle Energy Demand; Early Design Phases
series eCAADe
last changed 2020/09/09 09:52

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