Study Architecture 
ProPEL 
Author(s): Kelly Winn
To meet and respond to future challenges posed by large scale climate crises before they occur, it is necessary to begin addressing the adverse effects of the built environment by developing biological buffers or designing new systems to restore, replenish, and remediate ecological systems. Designers must pursue innovative methods to model, analyze, simulate, and optimize these systems, in order to design architecture and landscape systems that respond effectively to complex ecological problems occurring over significant temporal and geographic scales. As a result, data driven design is required to balance competing design criteria effectively through trade-offs to meet design objectives of high-performance architectural systems and is increasingly a requirement of design practice for the foreseeable future. This type of advanced computational design requires sophisticated models to be developed integrating these complicated tools in the design process for simulation and dynamic modeling. This type of advanced computation requires making use of readily available datasets defining weather profiles, material properties, energy performance, building physics, etc. that can all be used to inform the design process and test the suitability of design solutions. In this paper, a process for integrating multi-objective genetic algorithms (MOGA) for design optimization driving parametric models and environmental simulation is discussed. Three student case studies from a two-semester course sequence focusing on ecology-centric design schemes are presented, demonstrating the design optimization of sufficiently complex parametric models. These case studies will be used to demonstrate the potential for such methods to be incorporated in different aspects of the conceptual design process and architectural course curriculum.
https://doi.org/10.35483/ACSA.AM.106.38
Volume Editors
Amir Ameri & Rebecca O'Neal Dagg
ISBN
978-1-944214-15-9
