CHARGING will be held as part of the “7TH International Conference of Contemporary Affairs in Architecture and Urbanism” (ICCAUA2024) be represented by several contributions at the same time. The event will take place from 23 to 24 May 2024 at Alanya University in Turkey. Our presentations will highlight innovative approaches in the use of artificial intelligence and machine learning in architecture:
- Analysis of the use of artificial intelligence (AI) as a digital tool in the architectural design process (Tobias Hanke et al.) This article explores the use of artificial intelligence as a digital tool in the architectural design process. An architectural design study is being carried out to evaluate in which areas of the design process AI can be effectively used and where current limits lie. The study shows that AI is used in specific use cases of the design process and assesses its impact on the architectural design process. The results are based on a quantitative, practical study and provide insights into where the limits of AI use lie in the design process.
- Possibilities and limits of AI-supported design processes in architecture (Kawasaki et al.) The study highlights the impact of digital transformation in the construction industry on architects' design processes, with artificial intelligence (AI) contributing to automation, simplification and acceleration. The focus is on practical studies of form-finding processes with Grasshopper in Rhino. The process starts simply and gradually increases in geometric complexity. The AI generates the program code, which is integrated into Grasshopper to build the structure in Rhino. Partial results show generated codes and images of geometry and offer insights into structures and complexity. It is becoming clear that AI plays an important role for architects, can automate processes and remove barriers. The role of the architect is changing, and communication between architects and AI is critical to achieving the desired results.
- Integration of AI and LLMs for Enhanced BIM Analysis: A Study on .IFC Model Interpretation (Groenmeyer et al.) This study explores the application of artificial intelligence and large language models (LLMs) to interpret data from Building Information Modeling (BIM) in .ifc files. The aim is to make it possible to intuitively query model details such as area measurements by using these technologies.
The results of our research are presented in an online video session and published in a scientific paper in the conference proceedings.
Links and further information will follow after publication.
