Figure 7 - uploaded by Mesrop Andriasyan
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Optimisation attempts: each Cartesian axis corresponds to a fitness goal. Green boxes represent all of the attempts. Magenta boxes show selected solutions.
Source publication
The planning quality of refugee camps profoundly affects the people living there. Because of the short time span allotted to planners due to the state of emergency, camps are often poorly planned or not planned at all. This paper proposes tools and methods developed through computational modelling algorithms that can enhance the design procedure an...
Context in source publication
Context 1
... multi-objective optimisation algorithm creates a set of solutions that are optimised and can serve as a base model for the site planner to work with. Overall, several thousand attempts of the design were made (see figure 7). From these attempts, the designer can choose solutions that perform the best on either one of the goals or on average. ...
Citations
... Wallacei X has been used in recent studies to solve optimization problems at various scales, including robotic pavilion design and production (Mostafavi et al., 2020), robotic fabricationbased façade assembly (Ali et al., 2021), form and façade design for a group of buildings (Jansen and Piatek, 2020;Showkatbakhsh and Kaviani, 2021), emergency settlements and refugee camps planning (Andriasyan et al., 2020) and urban regeneration (Chen et al., 2022). ...
Purpose
This study aims to present a novel Genetic Algorithm-Based Design Model (GABDM) to provide reduced-risk areas, namely, a “safe footprint,” in interior spaces during earthquakes. This study focuses on housing interiors as the space where inhabitants spend most of their daily lives.
Design/methodology/approach
The GABDM uses the genetic algorithm as a method, the Nondominated Sorting Genetic Algorithm II algorithm, and the Wallacei X evolutionary optimization engine. The model setup, including inputs, constraints, operations and fitness functions, is presented, as is the algorithmic model’s running procedure. Following the development phase, GABDM is tested with a sample housing interior designed by the authors based on the literature related to earthquake risk in interiors. The implementation section is organized to include two case studies.
Findings
The implementation of GABDM resulted in optimal “safe footprint” solutions for both case studies. However, the results show that the fitness functions achieved in Case Study 1 differed from those achieved in Case Study 2. Furthermore, Case Study 2 has generated more successful (higher ranking) “safe footprint” alternatives with its proposed furniture system.
Originality/value
This study presents an original approach to dealing with earthquake risks in the context of interior design, as well as the development of a design model (GABDM) that uses a generative design method to reduce earthquake risks in interior spaces. By introducing the concept of a “safe footprint,” GABDM contributes explicitly to the prevention of earthquake risk. GABDM is adaptable to other architectural typologies that involve footprint and furniture relationships.
