id 
caadria2018_257 
authors 
Yousif, Shermeen and Yan, Wei 
year 
2018 
title 
Clustering Forms for Enhancing Architectural Design Optimization 
source 
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping  Proceedings of the 23rd CAADRIA Conference  Volume 2, Tsinghua University, Beijing, China, 1719 May 2018, pp. 431440 
summary 
This work introduces a new system in architectural design optimization that integrates form diversity and clustering methods into the process. The first method we propose is an algorithm for rating design solutions according to their geometric correspondences, maximizing differences and enforcing diversity. In addition, we implement the Kmeans algorithm to cluster the resulting design forms into groups of similar forms, to substitute each group with one representative solution. The work aims to facilitate decision making and form evaluation for designers, leading to an interactive optimization process, and contributing to improving existing optimization models in architectural design research and practice. We modeled a dynamic system through prototyping, experimenting and testcase application. As a prototype development, the protocol was done through phases of: (1) parametric modeling, (2) conducting energy simulation and daylight analysis and running a generative system, and (3) developing an algorithm for form diversity and another for implementing Kmeans clustering. The results are illustrated and discussed in detail in the paper. 
keywords 
Architectural Design Optimization; Form Diversity; KMeans Clustering 
series 
CAADRIA 
email 
shermeen@tamu.edu 
full text 
file.pdf (2,137,519 bytes) 
references 
Contenttype: text/plain

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last changed 
2018/05/17 07:08 
