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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, 17-19 May 2018, pp. 431-440
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 K-means 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 test-case 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 K-means clustering. The results are illustrated and discussed in detail in the paper.
keywords Architectural Design Optimization; Form Diversity; K-Means Clustering
series CAADRIA
email shermeen@tamu.edu
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