The place of generative systems in the architectural design process and the use of generative system in L-type kitchen design

The development in computer technologies has led to the integration of automatic/artificial intelligence in many fields. This technology, which has an increasingly widespread use, has also found its place in the architecture sector. With the inclusion of automatic/artificial intelligence in the design processes, different formatting approaches have been created. These approaches, which enable to achieve as generative design systems, offer many design options for the same product or the same space using a cloud and artificial intelligence-based system. While generative design systems that work depending on the rule sets consisting of parameters and constraints can ensure that the design is created within the desired limits; the design universe can be expanded by keeping the number of parameters and constraints low. In this way, the level of appreciation and satisfaction is increased by ensuring continuity in design options. Today, generative design systems, which are included in many Computer Aided Design (CAD) and Building Information Modeling (BIM) based programs and become accessible to more users, have also started to be used in interior design processes. In this way, while architects, interior architects and designers are gaining time and speed, it is ensured that the time, cost and raw material waste that may be experienced due to the problems that may be encountered in the application and production processes are minimized. In this study, the kitchen space, which is accepted as one of the most important areas in a house and is used intensively during the day, is discussed. In this context, it is aimed to produce L type kitchen alternatives by using tte generative design system and to evaluate the produced alternatives in terms of compliance with current kitchen design rules, functionality and ergonomics. ADeko, a native program that uses generative system software, was preferred for this study. A total of 24 L-type kitchen alternatives were produced by applying two different rule sets, and the success rate of each alternative in meeting the evaluation criteria was determined. As a result of the study, the average success rate in the primary design production where the restrictive parameters were applied was 63,88%; it was determined that the average success rate in the secondary design production where the restricrive parameters were removed was 62,21%. In addition, the lowest success rate among 24 alternatives was 46,66%; the highest success rate was found to be 80%. Accordingly, it has been concluded that the used automatic/artificial intelligence provides appreciation in terms of the degree of satisfaction and speed according to the expectation, but still requires human foresight and intervention. Keywords: Architectural design, Artificial intelligence, Generative design, Generative system, Kitchen design