Musial, A;
Singh Atwal, H;
Torero, JL;
(2025)
Quantification of building performance to evaluate novel design methods.
Proceedings of the Institution of Civil Engineers: Engineering Sustainability
01-88.
10.1680/jensu.24.00107.
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Abstract
A framework is proposed to characterize in a holistic and quantitative manner the performance of buildings. This framework aims at using continuous performance assessment, from the design conception through the life of a building. The quantitative assessment will allow designers to move away from thinking of buildings a prototype that provides no feedback loop to designers towards analysing buildings as living laboratories that inform novel design approaches. To understand the value of the framework and any factors limiting its application a biomimicry approach was used as an example. Biomimicry principles capitalize on evolution to deliver continuous quality improvement and therefore these principles were found to be consistent with modern design objectives. A series of high-profile buildings were analysed based on publicly available data. Six buildings were shortlisted for a more in-dept analysis, nevertheless it became evident that for all these buildings there was insufficient data analysis, against the design objectives, to enable a holistic assessment of performance. To illustrate the weaknesses in the data set, the best documented building, the Council House Building (CH2) in Melbourne, Australia was analysed and areas where data was missing were established.
| Type: | Article |
|---|---|
| Title: | Quantification of building performance to evaluate novel design methods |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1680/jensu.24.00107 |
| Publisher version: | https://doi.org/10.1680/jensu.24.00107 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Training , Robust control , Translation , Accuracy , Imitation learning , Scalability , Mathematical models , Data models , Robustness , Manipulator dynamics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10205028 |
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