| id |
caadria2024_259 |
| authors |
Pei, Wanyu, Xiong, Shuyan, Habert, Guillaume and Stouffs, Rudi |
| year |
2024 |
| title |
An Ontology-based Reasoning Framework: Towards Multi-level and Data-efficient Building Material Stock Modelling |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 335–344 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.335
|
| summary |
The materials stored in existing urban buildings represent a significant share of globally accumulated resources, the composition and quantity of which should be tracked for management and reuse purposes. Due to the coarse-grained nature of building data at the city level, the description of building material stock (BMS) is usually limited to the material intensity (MI) level of several key materials, omitting the component-level analysis of construction elements, and building devices. Hence, a flexible and compatible modelling framework is needed for BMS modelling to adopt different levels of detailed building data. This study proposes an ontology-based framework, which sets the characteristics of available building data as context and makes reasoning for a feasible modelling level. An ontology is developed to capture context knowledge and define the BMS concepts and their properties. A reasoning algorithm is designed to query and categorise building instances with the same attributes into an archetype, to integrate their various granularity of property data, and to calculate the material stocks at appropriate levels. Some Singapore buildings are used for ontology instantiation and explanation. This framework is anticipated to be a new paradigm for multi-level BMS modelling and contribute strategies for urban circularity design.08644890 |
| keywords |
circular city, building material stock, domain ontology, multi-level modelling, missing-data imputation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (3,391,309 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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