| id |
caadria2022_302 |
| authors |
Raghu, Deepika, Markopoulou, Areti, Marengo, Mathilde, Neri, Iacopo, Chronis, Angelos and De Wolf, Catherine |
| year |
2022 |
| title |
Enabling Component Reuse from Existing Buildings through Machine Learning, Using Google Street View to Enhance Building Databases |
| doi |
https://doi.org/10.52842/conf.caadria.2022.2.577
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 577-586 |
| summary |
Intense urbanization has led us to rethink construction and demolition practices on a global scale. There is an opportunity to respond to the climate crisis by moving towards a circular built environment. Such a paradigm shift can be achieved by critically examining the possibility of reusing components from existing buildings. This study investigates approaches and tools needed to analyze the existing building stock and methods to enable component reuse. Ocular observations were conducted in Google Street View to analyze two building-specific characteristics: (1) facade material and (2) reusable components (window, doors, and shutters) found on building facades in two cities: Barcelona and Zurich. Not all products are equally suitable for reuse and require an evaluation metric to understand which components can be reused effectively. Consequently, tailored reuse strategies that are defined by a priority order of waste prevention are put forth. Machine learning shows promising potential to visually collect building-specific characteristics that are relevant for component reuse. The data collected is used to create classification maps that can help define protocols and for urban planning. This research can upscale limited information in countries where available data about the existing building stock is insufficient. |
| keywords |
machine learning, component reuse, Google Street View, material banks, building databases, SDG 11, SDG 12 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,512,894 bytes) |
| references |
Content-type: text/plain
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Barahona, B., Buck, R., Okaya, O. & Schuetz, P. (2021)
 Detection of thermal anomalies on building faCades using infrared thermography and supervised learning
, Journal of Physics: Conference Series, 2042(1), 012013. https://doi.org/10.1088/1742-6596/2042/1/012013
|
|
|
|
|
Debacker, W., Manshoven, S. & Denis, F. (2016)
D1 Synthesis of the state-of-the-art: Key barriers and opportunities for Materials Passports and Reversible Building Design in the current system
, BAMB Horizon 2020
|
|
|
|
|
Duan, H., Miller, T. R., Liu, G. & Tam, V. W. Y. (2019)
Construction debris becomes growing concern of growing cities
, Waste Management, 83, 1–5. https://doi.org/10.1016/j.wasman.2018.10.044
|
|
|
|
|
Dubey, A., Naik, N., Parikh, D., Raskar, R. & Hidalgo, C. A. (2016)
Deep Learning the City: Quantifying Urban Perception at a Global Scale
, B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision – ECCV 2016 (pp. 196–212). Springer International Publishing. https://doi.org/10.1007/978-3-319-46448-0_12
|
|
|
|
|
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L. & Fei-Fei, L. (2017)
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States
, Proceedings of the National Academy of Sciences, 114(50), 13108–13113. https://doi.org/10.1073/pnas.1700035114
|
|
|
|
|
Ma, M., Tam, V. W. Y., Le, K. N. & Li, W. (2020)
Challenges in current construction and demolition waste recycling: A China study
, Waste Management, 118, 610–625. https://doi.org/10.1016/j.wasman.2020.09.030
|
|
|
|
|
Rose, C. & Stegemann, J. (2018)
Characterising Existing Buildings as Material Banks (E-BAMB) to Enable Component Reuse
, Proceedings of the Institution of Civil Engineers - Engineering Sustainability, 172, 1–42. https://doi.org/10.1680/jensu.17.00074
|
|
|
|
|
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. (2019)
MobileNetV2: Inverted Residuals and Linear Bottlenecks
, ArXiv:1801.04381 [Cs]. http://arxiv.org/abs/1801.04381
|
|
|
|
|
Soibelman, L., Wu, J., Caldas, C., Brilakis, I. & Lin, K.-Y. (2008)
Management and analysis of unstructured construction data types
, Advanced Engineering Informatics, 22, 15–27. https://doi.org/10.1016/j.aei.2007.08.011
|
|
|
|
|
Teboul, O., Simon, L., Koutsourakis, P. & Paragios, N. (2010)
Segmentation of building facades using procedural shape priors
, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2010.5540068
|
|
|
|
|
Uotila, U., Saari, A. & Junnonen, J. M. (2021)
Investigating the barriers to laser scanning implementation in building refurbishment
, Journal of Information Technology in Construction, 26, 249–262. https://doi.org/10.36680/j.itcon.2021.014
|
|
|
|
|
von Platten, J., Sandels, C., Jörgensson, K., Karlsson, V., Mangold, M. & Mjörnell, K. (2020)
Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits
, Energies, 13, 2574. https://doi.org/10.3390/en13102574
|
|
|
|
|
Šekularac, N., Ivanovic-Sekularac, J., Petrovski, A., Macut, N. & Radojević, M. (2020)
Restoration of a Historic Building in Order to Improve Energy Efficiency and Energy Saving—Case Study—The Dining Room within the Žiča Monastery Property
, Sustainability, 12, 6271. https://doi.org/10.3390/su12156271
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| last changed |
2022/07/22 07:34 |
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