Optimizing the Residential Design Process Using Automated Technologies

Authors: Milena Nanova, Martin Georgiev, Damyan Damov

Abstract:

Modern residential architecture is increasingly influenced by rapid urbanization, technological advancements, and growing investor expectations. The integration of AI and digital tools such as CAD and BIM (Building Information Modelling) is transforming the design process by improving efficiency, accuracy, and speed. However, urban development faces challenges, including the high competition for viable sites and the time-consuming nature of traditional investment feasibility studies and architectural planning. Finding and analyzing suitable sites for residential development is complicated by intense competition and rising investor demands. Investors require quick assessments of property potential to avoid missing opportunities, while traditional architectural design processes rely on the experience of the team and can be time-consuming, adding pressure to make fast, effective decisions. The widespread use of CAD tools has sped up the drafting process, enhancing both accuracy and efficiency. Digital tools allow designers to manipulate drawings quickly, reducing the time spent on revisions. BIM further advances this by enabling native 3D modelling, where changes to a design in one view are automatically reflected in all others, minimizing errors and saving time. AI is becoming an integral part of architectural design software. While AI is currently being incorporated into existing programs like AutoCAD, Revit, and ArchiCAD, its full potential is reached in parametric modelling. In this process, designers define parameters (e.g., building size, layout, and materials), and the software generates multiple design variations based on those inputs. This method accelerates the design process by automating decisions and enabling the quick generation of alternative solutions. The study utilizes generative design, a specific application of parametric modelling that uses Machine Learning (ML) to explore a wide range of design possibilities based on predefined criteria. It optimizes designs through iterations, testing many variations to find the best solutions. This process is particularly beneficial in the early stages of design, where multiple options are explored before refining the best ones. ML’s ability to handle complex mathematical tasks allows it to generate unconventional yet effective designs that a human designer might overlook. Residential architecture, with its anticipated and typical layouts and modular nature, is especially suitable for generative design. The relationships between rooms and the overall organization of apartment units follow logical patterns, making it an ideal candidate for parametric modelling. Using these tools, architects can quickly explore various apartment configurations, considering factors like apartment sizes, types, and circulation patterns, and identify the most efficient layout for a given site. Parametric modelling and generative design offer significant benefits to residential architecture by streamlining the design process, enabling faster decision-making, and optimizing building layouts. These technologies allow architects and developers to analyze numerous design possibilities, improving outcomes while responding to the challenges of urban development. By integrating ML-driven generative design, the architecture industry can enhance creativity, efficiency, and adaptability in residential projects.

Keywords: architectural design, generative design, parametric models, residential buildings, workflow optimization