This paper presents a methodology leveraging modern technologies such as BIM, laser scanning, and virtual reality to address challenges in maintaining historical buildings. The focus is on a scan-vs-BIM deviation analysis workflow, enabling the identification of modelling inaccuracies and structural issues by comparing HBIM models with point-cloud surveys. The proposed approach facilitates decision-making processes by providing an accessible and detailed visualization of results. The methodology begins with a structured BIM model and a corresponding point cloud. Using Autodesk Revit and a tailored Dynamo script, distances between building elements and their surveyed points are calculated and organized for compatibility with various platforms. The results are then transformed into interactive 3D visualizations using Python, where points are spatially and color-coded based on deviation values. The workflow integrates immersive technologies, such as virtual reality, to explore the results interactively and at real scale, enhancing insights beyond traditional 2D graphs and screen-based methods. This immersive visualization highlights critical details and supports improved decision-making in structural analysis and conservation efforts. The paper also discusses the potential for augmented reality to further enhance these visualizations, offering direct comparisons between BIM models, point clouds, and the physical building. With a BIM-based and open-source approach, the methodology ensures broad accessibility and reusability, making it a robust tool for deviation analysis and visualization in heritage conservation projects.
Strategic Classification of the Integration Between Artificial Intelligence (AI) and Building Information Modelling (BIM): Opportunities and Future Challenges
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.
Data Structure for Cultural Heritage. Paintings from BIM to Social Media AR
This paper focuses on a process to communicate and enhance cultural heritage value. In this context, one of the main challenges is to combine its value with digital strategies and methods without losing information and increasing communication and public-private involvement. The paper proposes a methodology that uses BIM (Building Information Modeling) and CDE (Common Data Environment) concepts to build and organize information of paintings through connected databases, typically pro-duced by multiple actors. A case study in San Nicolò in Carpi verifies its application. An Instagram pro-file has been created transferring data from BIM models to Spark AR Studio to demonstrate a meth-od that creates an Augmented Reality application for cultural heritage, without the need of coding.
Automatic Recognition Through Deep Learning of Standard Forms in Executive Projects
In this paper is presented a possible methodology for automation through the use of deep learning of BIM modeling starting from different types of formats, such as digital processing of paper documents and CAD formats. The work is configured as a proof of concept of a possible contribution that a technique currently scarcely used in the architectural field such as deep learning can bring to the design, in particular in the realization of the information model, which today represents one of the most consuming–time activities.