The design of residential care facilities for individuals with dementia profoundly impacts their quality of life and wellbeing. Dementia-friendly architecture, thoroughly reviewed in literature, provides guidelines and assessment tools to evaluate residential spaces and enhance living conditions. Key to residents' wellbeing is their autonomy and control over their environment, which can be facilitated by optimizing wayfinding within indoor spaces. Effective spatial layouts, particularly those offering good visual access, not only promote autonomy but also improves social integration by enabling residents to see and be seen by others.

This MSc thesis investigates the feasibility of artificial intelligence (AI) to support the design of dementia-friendly architecture, focusing particularly on wayfinding—a critical element of environmental design for individuals with dementia. The study quantitatively assesses the relationship between floor plan layouts and wayfinding ease using the isovist method, linking floor plan geometry with the navigational experiences of dementia patients. The assessment was done in accordance with an established Dementia Design Principles (DDP) environmental assessment tool recognized within universal design guidelines.

A computational framework was developed to evaluate wayfinding quality using visual access analysis, which were integrated into a machine learning model. This model was trained on a dataset of 256 floor plans, employing features derived from two distinct sources: spatial metrics such as distances and centrality from the Swiss Dwellings dataset, and compactness and distance-based features extracted via Grasshopper, a visual scripting tool in Rhino 3D. The model was tested using two supervised machine learning algorithms—Random Forest (RF) and Artificial Neural Networks (ANN)—and achieved consistent accuracy rates between 70-80% using 14 features and 2 multiclass outputs describing the visual access quality. This demonstrates AI's potential as a decision-support tool in the early stages of architectural design, offering architects insights into the wayfinding quality of their designs.

The goal of this research is to develop a digital framework that can be leveraged by architects to link early-stage concept design ideation to the specialist validation of final designs to help guide the design of layouts towards DDP-compliance and reduce risk of design changes, ultimately designs that are easier to navigate by people living with dementia and enhance the quality of living.