| id |
caadria2024_234 |
| authors |
Xiong, Shuyan, Zea Escamilla, Edwin and Habert, Guillaume |
| year |
2024 |
| title |
Uncovering the Circular Potential: Estimating Material Flows for Building Systems Components Reuse in the Swiss Built Environment |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.545
|
| 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 1, pp. 545–554 |
| summary |
The construction industry plays a critical role in global resource consumption and greenhouse gas emissions, highlighting the urgent need for sustainable development practices. However, a key challenge in this area is the lack of effective models for resource use that align with circular economy principles. This gap hinders efforts to achieve sustainable resource management, especially in the face of increasing urbanization and material demand. To address this issue, our study presents a Parametric Predictive Model (PPM) to improve resource efficiency, specifically targeting the often-underestimated building systems. The model takes a bottom-up approach, utilizing local databases to accurately assess material stocks of building systems, thereby improving the granularity of data on material composition. Using advanced machine learning algorithms, the model processes both categorical and non-categorical data. The output, an enriched comprehensive database can support more informed decision making in sustainable resource recovery and allocation, but also contribute to the broader goals of reducing waste and promoting resource efficiency in the built environment. |
| keywords |
Building Systems, Building Stock Modelling, Predictive Model, Circular Economy, Parametric Model |
| series |
CAADRIA |
| email |
xiong@ibi.baug.ethz.ch |
| full text |
 file.pdf (579,765 bytes) |
| references |
Content-type: text/plain
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D'Alonzo, V., Novelli, A., Vaccaro, R., Vettorato, D., Albatici, R., Diamantini, C., & Zambelli, P. (2020)
 ClimateStudio Documentation
, Energy and Buildings, 206, 109581. https://doi.org/10.1016/j.enbuild.2019.109581
|
|
|
|
|
Hamilton, I., Kennard, H., Rapf, O., Kockat, J., Zuhaib, S., Abergel, T., Oppermann, M., Otto, M., Loran, S., Steurer, N., & others. (2020)
A History of Parametric
, United Nations Environment Programme, Ef-ficient and Resilient Buildings and Construction Sector: Nairobi, Kenya
|
|
|
|
|
Hoxha, E., Maierhofer, D., Saade, M. R. M., & Passer, A. (2021)
Home
, Inter-national Journal of Life Cycle Assessment, 26(5), 852-863. https://doi.org/10.1007/s11367-021-01919-9
|
|
|
|
|
Leao, S., Bishop, I., & Evans, D. (2001)
Assessing the demand of solid waste disposal in urban region by urban dynamics modelling in a GIS environment
, Resources, Conservation and Recycling, 33(4), 289-313. https://doi.org/10.1016/S0921-3449(01)00090-8
|
|
|
|
|
Office, F. S. (2023)
Machine learning: A probabilistic perspective
, Federal Statistical Office. https://www.bfs.admin.ch/asset/en/27905171
|
|
|
|
|
Pasichnyi, O., Wallin, J., & Kordas, O. (2019)
Data-driven building archetypes for urban building energy modelling
, Energy, 181, 360-377. https://doi.org/10.1016/j.energy.2019.04.197
|
|
|
|
|
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., ... Chintala, S. (2019)
PyTorch: An Imperative Style, High-Performance Deep Learning Library
, Advances in Neural Information Processing Systems 32 (pp. 8024-8035). Curran Associates, Inc. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
|
|
|
|
|
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011)
Scikit-learn: Machine Learning in Py-thon
, Journal of Machine Learning Research, 12, 2825-2830
|
|
|
|
|
Röck, M., Hollberg, A., Habert, G., & Passer, A. (2018)
LCA and BIM: Visualization of environmental potentials in building construc-tion at early design stages
, Building and Environment, 140(December 2017), 153-161. https://doi.org/10.1016/j.buildenv.2018.05.006
|
|
|
|
|
Verellen, E., & Allacker, K. (2020)
Mitigation pathways compatible with 1
, IOP Conference Series: Earth and Environmental Science, 588(3), 032006. https://doi.org/10.1088/1755-1315/588/3/032006
|
|
|
|
|
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., ... SciPy 1.0 Contributors. (2020)
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
, Nature Methods, 17, 261-272. https://doi.org/10.1038/s41592-019-0686-2
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| last changed |
2024/11/17 22:05 |
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