| id |
ecaade2015_55 |
| authors |
Chen, KianWee; Janssen, Patrick and Schlueter, Arno |
| year |
2015 |
| title |
Analysing Populations of Design Variants Using Clustering and Archetypal Analysis |
| doi |
https://doi.org/10.52842/conf.ecaade.2015.1.251
|
| source |
Martens, B, Wurzer, G, Grasl T, Lorenz, WE and Schaffranek, R (eds.), Real Time - Proceedings of the 33rd eCAADe Conference - Volume 1, Vienna University of Technology, Vienna, Austria, 16-18 September 2015, pp. 251-260 |
| wos |
WOS:000372317300027 |
| summary |
In order to support exploration in the early stages of the design process, researchers have proposed the use of population-based multi-objective optimisation algorithms. This paper focuses on analysing the resulting population of design variants in order to gain insights into the relationship between architectural features and design performance. The proposed analysis method uses a combination of k-means clustering and Archetypal Analysis in order to partition the population of design variants into clusters and then to extract exemplars for each cluster. The results of the analysis are then visualised as a set of charts and as design models. A demonstration of the method is presented that explores how self-shading geometry, envelope materials, and window area affect the overall performance of a simplified building type. The demonstration shows that although it is possible to derive general knowledge linking architectural features to design performance, the process is still not straightforward. The paper ends with a discussion on how the method can be further improved. |
| series |
eCAADe |
| email |
|
| more |
https://mh-engage.ltcc.tuwien.ac.at/engage/ui/watch.html?id=09a711e6-70f5-11e5-af69-2b8082624d42 |
| full text |
 file.pdf (4,402,653 bytes) |
| references |
Content-type: text/plain
|
BCA, S (2013)
 BCA Green Mark for New Non-Residential Buildings Version NRB/4.1
, Building and Construction Authority Singapore
|
|
|
|
|
Caldas, L (2008)
Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system
, Advanced Engineering Informatics, 22, pp. 59-70
|
|
|
|
|
Chichakly, K and Eppstein, M (2013)
Discovering Design Principles from Dominated Solutions
, Access, IEEE, 1, pp. 275-289
|
|
|
|
|
Chua, KJ and Chou, SK (2010)
An ETTV-based approach to improving the energy performance of commercial buildings
, Energy and Buildings, 42, pp. 491-499
|
|
|
|
|
Cutler, A and Breiman, L (1994)
Archetypal Analysis
, Technometrics, 36(4), pp. 338-347
|
|
|
|
|
Deb, K and Srinivasan, A (2006)
Innovization: Innovating Design Principles Through Optimization
, Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, New York, NY, USA, pp. 1629-1636
|
|
|
|
|
Deb, K, Agrawal, S, Pratap, A and Meyarivan, T (2000)
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
, Schoenauer, M, Deb, K, Rudolph, G, Yao, X, Lutton, E, Merelo, J and Schwefel, HP (eds), Parallel Problem Solving from Nature PPSN VI, Springer Berlin Heidelberg, pp. 849-858
|
|
|
|
|
Deb, K, Bandaru, S, Greiner, D, Gaspar-Cunha, A and Tutum, CC (2014)
An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering
, Applied Soft Computing, 15, pp. 42-56
|
|
|
|
|
Everitt, B and Hothorn, T (2011)
Cluster Analysis
, Everitt, B and Hothorn, T (eds), An Introduction to Applied Multivariate Analysis with R, Springer New York, New York, pp. 163-200
|
|
|
|
|
Flager, F, Welle, B, Bansal, P, Soremekun, G and Haymaker, J (2009)
Multidisciplinary Process Integration and Design Optimisation of a Classroom Building
, Journal of Information Technology in Construction, 14, pp. 595-612
|
|
|
|
|
Han, J, Kamber, M and Pei, J (2012)
10 - Cluster Analysis: Basic Concepts and Methods
, Han, J, Kamber, M and Pei, J (eds), Data Mining (Third Edition), Morgan Kaufmann, Boston, pp. 443-495
|
|
|
|
|
Harfield, S (2007)
On design 'problematization': Theorising differences in designed outcomes
, Design Studies, 28(2), pp. 159-173
|
|
|
|
|
Hartigan, JA (1975)
Clustering Algorithms
, John Wiley & Sons, New York
|
|
|
|
|
Janssen, P, Basol, C and Chen, KW (2011)
Evolutionary Developmental Design for Non-Programmers
, 29th eCAADe Conference Proceedings, University of Ljubljana, Faculty of Architecture (Slovenia), pp. 245-252
|
|
|
|
|
Lawson, B (2004)
What designers know
, Architectural Press, Oxford
|
|
|
|
|
Lin, SHE and Gerber, DJ (2014)
Designing-in performance: A framework for evolutionary energy performance feedback in early stage design
, Automation in Construction, 38, pp. 59-73
|
|
|
|
|
Mela, K, Tiainen, T and Heinisuo, M (2012)
Comparative study of multiple criteria decision making methods for building design
, EG-ICE 2011 + SI: Modern Concurrent Engineering, 26(4), pp. 716-726
|
|
|
|
|
Pohekar, S and Ramachandran, M (2004)
Application of multi-criteria decision making to sustainable energy planning-A review
, Renewable and Sustainable Energy Reviews, 8(4), pp. 365-381
|
|
|
|
|
Singhaputtangkul, N, Low, SP, Teo, AL and Hwang, BG (2013)
Knowledge-based Decision Support System Quality Function Deployment (KBDSS-QFD) tool for assessment of building envelopes
, Automation in Construction, 35, pp. 314-328
|
|
|
|
|
Turrin, M, Buelow, Pv and Stouffs, R (2011)
Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms
, Advanced Engineering Informatics, 25, pp. 656-675
|
|
|
|
| last changed |
2022/06/07 07:55 |
|
