The integration of Artificial Intelligence (AI) and Building Information Modelling (BIM) represents a promising but complex frontier in the construction industry. While BIM has already transformed construction workflows through digitalization and lifecycle management, AI has the potential to further advance automation, optimization, and data-driven decision-making. This study aims to provide a systematic classification of AI applications within BIM, identifying four key categories: AI as a digital consultant, collaborative interface, process regulator, and process outcome. Through a scientometric analysis of literature and structured mapping of existing software, the paper evaluates the state of the art, exploring the capabilities and limitations of current solutions. Results highlight how AI + BIM tools are transforming various lifecycle stages, from conceptual design to construction and operations. However, challenges remain, including the lack of standardization, risks related to data security, and the balance between automation and human oversight. This classification framework not only structures existing knowledge but also directs future research and applications, encouraging critical reflection on AI’s role in advancing BIM methodologies while considering its implications for transparency, efficiency, and technological governance.
A Method for Conscious Retrofitting Based on Handheld Laser Scanner and Environmental Data
The research behind this paper focuses on the connection between monitoring the environmental quality of a sample building and the corresponding digital model. The data obtained from the sample will be extended to a related typology of buildings, thus generating a method for conscious retrofitting of a large portion of the built environment. To conduct this study, a digital ecosystem comprising the model and a series of environmental sensors has been prepared. Within this digital environment, it is possible to visualize the data acquired from real-life sensors directly into the model. The digital twin method, in which there is a direct cause/effect correspondence between the real artifact and its digital alter ego, is foundational to this experimentation. Environmental data gathered from the building interacts with the digital model and returns to the physical reality as indicators. Once these indicators have been verified on the sample, they can be implemented in other related buildings. Sensors measuring temperature, pressure, humidity, and light radiation were concurrently applied to the building. The data flow is bidirectional, from the sensors to the model and vice versa. In the initial direction, the sensors transmit data to the digital model through a series of steps. The sensors communicate with a digital ecosystem. Once the procedure has been verified to work, it can be replicated easily, since open-source Arduino components were used for the sensor system. These components, besides being easy to find and economically viable, can be adapted and reproduced with extreme simplicity because, according to the Arduino philosophy, both the operating codes and the electronic production diagrams of the individual components are freely available.
New Representation Tools in VR and Holographic View
This paper investigates the development of advanced representation tools based on virtual reality and holographic visualization to enhance the communication, interpretation, and understanding of architectural and cultural heritage models. The research addresses the limitations of traditional two-dimensional and screen-based representations, proposing immersive and multi-user environments as more effective means for conveying spatial complexity to both expert and non-expert audiences.
The methodology is based on the integration of 3D modeling workflows (Revit, Rhino, SketchUp) with real-time visualization environments developed in Unity, using Unity Reflect to preserve geometric and semantic data throughout the pipeline (Fig. 3, p. 813). The system supports both VR headsets and holographic tables, enabling immersive single-user experiences and collaborative multi-user interaction. Several custom tools are implemented, including annotation systems for collaborative review, dynamic section planes for real-time spatial analysis (Fig. 9, p. 816), and heatmap visualizations that map quantitative parameters onto model components (Fig. 11, p. 818).
Results demonstrate that immersive and holographic visualization significantly improve spatial perception, data interpretation, and collaborative workflows, particularly in educational and design contexts. The study highlights the potential of integrating real-time engines and extended reality technologies into architectural representation pipelines, while emphasizing that these tools act primarily as enhanced visualization and interaction systems rather than generative or analytical AI processes.
Enhanced Interaction Experience for Holographic Visualization
Nowadays, holographic visualization pushes further the limits in exploring tri-dimensional digital con-tent. 3D models typically displayed through a computer screen now enter the real world as holograms. The Hologram Table allows users to visualize and manipulate huge 3D models as if they were in the space in front of them. Its use has already proved helpful for the virtual fruition and presentation of complex cultural heritage buildings and their design interventions, but it surely can do more. The work aims at exploring the possibility of expanding the capabilities of the Hologram Table interaction by developing a custom-designed experience to interact with 3D point cloud data coming from survey activity. The test case was the interesting building of the Santa Maria delle Grazie (Milan, Italy) basilica. Initial results are encouraging and show that the point model can be enriched with associated informa-tion and additional content (images and texts) available for holographic visualization.
Limitations and Review of Geometric Deep Learning Algorithms for Monocular 3D Reconstruction in Architecture
This paper aims to test algorithms for 3D reconstruction from a single image specifically for building envelopes. This research shows the current limitations of these approaches when applied to classes outside of the initial distribution. We tested solutions with differentiable rendering, implicit functions, and other end–to–end geometric deep learning approaches. We recognize the importance of gener-ating a 3D reconstruction from a single image for many different industries, not only for Architecture, Engineering, and Construction (AEC) industry but also for robotics, autonomous driving, gaming, virtual and augmented reality, drone delivery, 3D authoring, improving 2D recognition and many others. Henceforth, engineers and computer scientists could benefit, not only from having the 3D representations but also from the Building Information Model (BIM) at their disposal. With further development of these algorithms it could be possible to access specific properties such as thermal, physical, maintenance, cost, and other parameters embedded in the class.