This paper elucidates an approach that utilizes generative artificial intelligence (AI) to develop alternative architectural design options based on local identity. The advancement of AI technologies has increasingly piqued the interest of the architecture, engineering, construction, and facility management industry. Notably, the topic of “visualization” has gained prominence as a means for enhancing communication related to a project, especially in the early phases of design. This study aims to enhance the ease of obtaining design images during initial phases of design by drawing from multiple texts and images. It develops an additional training model to generate various design alternatives that resonate with the identity of the locale through the application of generative AI to the façade design of buildings. The identity of a locality in cities and regions is the capacity for the cities and regions to be identified and recognized as a specific area. Among the various visual elements of urban and regional landscapes, the front face of buildings may play a significant role in people’s aesthetic perception and overall impression of the local environment. The research proposes an approach that transcends the conventional employment of three-dimensional modeling and rendering tools by readily deriving design alternatives that consider this local identity in commercial building remodeling. This approach allows for financial and temporal efficiency in the design communication phase of the initial architectural design process. The implementation and utilization of the proposed approach’s supplementary training model in this study proceeds as follows: (i) image data are collected from the target area using open-source street-view resources and preprocessed for conversion to a trainable format; (ii) textual data are prepared for pairing with preprocessed image data; (iii) additional training and outcome testing are performed using varied text prompts and images; and (iv) the ability to generate building façade images that reflect the identity of the collected locale by using the additional trained model is determined, as evidenced by the findings of the proposed application method study. This enables the generation of design alternatives that integrate regional styles and diverse design requirements for buildings. The training model implemented in this study can be leveraged through weight adjustments and prompt engineering to generate a greater number of design reference images, among other diverse approaches.

This paper describes an approach for identifying and appending interior design style information stochastically with reference images and a deep-learning model. In the field of interior design, design style is a useful concept and has played an important role in helping people understand and communicate interior design. Previous studies have focused on how the interior design style categories can be defined. On the other hand, this paper focuses on how stochastically recognizing the design style of given interior design reference images using a deep learning-based data-driven approach. The proposed method can be summarized as follows: (1) data preparation based on a general design style definition, (2) implementing an interior design style recognition model using a pre-trained VGG16 model, (3) training and evaluating the trained model, and (4) demonstration of stochastic detection of interior design styles for reference images. The result shows that the trained model automatically recognizes the design styles of given interior images with probability values. The recognition results, model, and trained image set contribute to facilitating the management and utilization of an interior design references database.

This paper discusses an approach to augmented virtual reality (AVR) and 360-degree spatial visualization. The approach involves locating stereoscopic three-dimensional virtual objects into a real off-site panorama, supporting spatial remodel design decision-making through realistic comparisons. Previous studies have shown that in the design process, end-user engagement promotes the quality and satisfaction of design solutions. Immersive media such as virtual reality (VR) and augmented reality (AR) have increasingly been used as communication tools for user engagement in design, as they provide intuitive and realistic user experiences, particularly in comparing design plans. However, the dichotomous affordance of current VR and AR devices is limited in satisfying both the sense of realism and immersion that are essential for user engagement. To overcome this shortcoming, we propose an AVR-based design visualization approach that integrates the advantages of both media technologies to provide a high sense of realism and immersion off-site, responding to location and environmental stimuli, such as lighting, material, and other factors. To achieve this goal, we used 360-degree panorama data of the target space as a design visualization background, with content immersion experienced through VR hardware. Additionally, we developed software to demonstrate the actual use of the AVR-based approach, and various visualization-purposed file formats can be exported automatically using this software. The software supports the authoring of 360-degree spatial visualization videos for realistic design comparisons, which can be easily accessed by end-users using a head-mounted display or smartphone, even in real-time. We performed a demonstration of this approach using an actual remodel design project for the university library lobby, and this paper shows the usability and applicability of the AVR-based approach for user engagement.

Emergency exit signs have an important role in the fire safety of buildings. Exit signs help evacuees rapidly escape fire by following the fastest and safest escape routes immediately after the detection of fire. In other words, evacuation can greatly vary according to environmental factors regarding evacuation within the building. In this study, change in the evacuation speed by exit signs and environmental conditions was analyzed through experiments with 138 subjects. Four environmental factors in the experiment are visibility, distance between exit sign and evacuees, spatial configuration, and size and brightness of exit sign. In conclusion, changes in the spatial conditions around exit signs influenced the evacuation speeds of the subjects in poor visibility, and the changes in physical conditions of exit signs exerted more influence when the visibility was relatively better.

This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, and construction industry. By creating more than 10 000 images incorporating an architect’s personal style and characteristics into a residential house model, the effectiveness of base AI models. Furthermore, various architectural styles were integrated to enhance the visualization process. This method involved additional training for styles with low similarity rates, which required extensive data preparation and their integration into the base AI model. Demonstrated to be effective across multiple scenarios, this technique markedly enhances the efficiency and speed of production of architectural visualization images. Highlighting the vast potential of AI in design visualization, our study emphasizes the technology’s shift toward facilitating more user-centered and personalized design applications.