| id |
ecaade2020_015 |
| authors |
Yazici, Sevil |
| year |
2020 |
| title |
A machine-learning model driven by geometry, material and structural performance data in architectural design process |
| source |
Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 411-418 |
| doi |
https://doi.org/10.52842/conf.ecaade.2020.1.411
|
| summary |
Artificial Intelligence (AI), based on interpretation of data, influences various professions including architectural design today. Although research on integrating conceptual design with Machine Learning (ML) algorithms as a subset of the AI has been investigated previously, there is not a framework towards integration of architectural geometry with material properties and structural performance data towards decision making in the early-design phase. Undertaking performance simulations require significant amount of computation power and time. The aim of this research is to integrate ML algorithms into design process to achieve time efficiency and improve design results. The proposed workflow consists of three stages, including generation of the parametric model; running structural performance simulations to collect the data, and operating the ML algorithms, including Artificial Neural Network (ANN), Non-Linear Regression (NLR) and Gaussian Mixture (GM) for undertaking different tasks. The results underlined that the system generates relatively fast solutions with accuracy. Additionally, ML algorithms can assist generative design processes. |
| keywords |
Machine-learning; performance simulation; data-driven design; early-design phase |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,252,694 bytes) |
| references |
Content-type: text/plain
|
As, I., Pal, S. and Basu, P. (2018)
 Artificial intelligence in architecture: Generating conceptual design via deep learning
, International Journal of Architectural Computing, 16(4), pp. 306-327
|
|
|
|
|
Brown, N.C and Mueller, C. T (2019)
Design variable analysis and generation for performance-based parametric modeling in architectur
, International Journal of Architectural Computing, 17(1), pp. 36-52
|
|
|
|
|
Chatzikonstantinou, I. and Sariyildiz, S. (2016)
Approximation of simulation-derived visual comfort indicators in office spaces: a comparative study in machine learning
, Architectural Science Review, vol. 59, No. 4, pp. 307-322
|
|
|
|
|
Derix, C. and Jagannath, P. (2014)
Near Futures: Associative Archetypes
, Architectural Design, 84(5), pp. 130-135
|
|
|
|
|
Karan, E. and Asadi, S. (2019)
Intelligent designer: A computational approach to automating design of windows in buildings.
, Automation in Construction, 102, pp. 160-169
|
|
|
|
|
Negroponte, N (1969)
Toward a Theory of Architecture Machines
, Journal of Architectural Education, 23, pp. 9-12
|
|
|
|
|
Shubert, B.O (1969)
Bayesian Model of Decision-Making as a Result of Learning From Experience
, Annals of Mathematical Statistics, 40, pp. 2127-2142
|
|
|
|
|
Storey, V. C. and Goldstein, R.C. (1993)
Knowledge-Based Approaches to Database Design
, MIS Quarterly, 17, pp. 25-46
|
|
|
|
|
Strobbe, T., Wyffels, F., Verstraeten, R., Meyer, R. D. and Campenhout, J. V. (2016)
Automatic architectural style detection using one-class support vector machines and graph kernels
, Automation in Construction, 69, pp. 1-10
|
|
|
|
|
Tamke, M. and Thomsen, M. R. (2018)
Complex Modelling
, International Journal of Architectural Computing, 16(2), pp. 87-90
|
|
|
|
|
Tamke, M., Nicholas, P., P. and Zwierzycki, M. (2018)
Machine learning for architectural design: Practices and infrastructure.
, International Journal of Architectural Computing, 16(2), pp. 123-143
|
|
|
|
|
Tseranidis, S., Brown, N.C. and Mueller, C.T. (2016)
Data-driven approximation algorithms for rapid performance evaluation and optimization of civil structures
, Automation in Construction, 72, pp. 279-293
|
|
|
|
|
Tseranidis, S. (2015)
Approximation Algorithms for Rapid Evaluation and Optimization of Architectural and Civil Structures
, Master's Thesis, Massachusetts Institute of Technology
|
|
|
|
| last changed |
2022/06/07 07:57 |
|
