Coevolutionary and genetic algorithm based building spatial and structural design

Hofmeyer, H�rm; Davila Delgado, Juan Manuel

Abstract

© Cambridge University Press 2015. In this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.

• Presentation Conference Type: Conference Paper (published)
• Acceptance Date: Apr 2, 2015
• Online Publication Date: Oct 7, 2015
• Publication Date: Jan 1, 2015
• Deposit Date: Feb 18, 2019
• Journal: Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
• Print ISSN: 0890-0604
• Electronic ISSN: 1469-1760
• Publisher: Cambridge University Press (CUP)
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 04
• Pages: 351-370
• DOI: https://doi.org/10.1017/S0890060415000384
• Public URL: https://uwe-repository.worktribe.com/output/804680
• Publisher URL: http://dx.doi.org/10.1017/S0890060415000384
• Contract Date: Feb 18, 2019