| id |
acadia23_v2_44 |
| authors |
Pei, Wanyu; Stouffs, Rudi |
| year |
2023 |
| title |
Parametric Archetype: A Synthetic Digital Method of Buildings Material Stock Representation Based on Distance Measurement |
| source |
ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 44-52. |
| summary |
Building material stock (BMS) is a crucial inventory of secondary resources which contain comprehensive information for analyzing the potential of material reuse and urban harvesting. Due to the complexity of urban building systems and the large number of buildings, obtaining building information one by one is impractical. Existing methods for stock representation mainly start from data collection, and utilize techniques such as clustering, machine learning, computer vision, et cetera, to process and analyze large and complete datasets. However, it is noticed that data on urban buildings, especially for building materials, is very limited or rather inaccessible. Existing methods cannot be applied in data-scarce cities and are also challenging to update over time. Therefore, this study proposes a synthetic approach named parametric archetype for the digital repre- sentation of BMS. This approach combines distance measurement, which is a distance within dimensions describing building features, to match instance buildings dynamically to a parametric archetype with the highest similarity. The weight and types of different building features, which may influence building material (composition and properties) in distance measurement, can be determined by supervised, semi-supervised, or unsuper- vised learning, whether relying on ample available data or domain rules/expert knowledge when data is scarce. This way, the parametric archetype model can use data more effi- ciently to form a synthetic and extensible representation for urban-level BMS (Figure 1). The parametric archetype is anticipated to offer an approach for describing, quantifying, and modeling the real building material stock system incrementally and transparently. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (2,674,814 bytes) |
| references |
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| last changed |
2024/12/20 09:12 |
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