HBIM technologies demonstrate increased potential in managing existing built heritage, leading to improved building lifecycle engineering. However, applying HBIM protocols to Cultural Heritage, particularly museum assets, represents an outstanding question. The study provides the findings of a research project conducted at the Galleria Borghese Museum to implement a geometric and informative BIM-based digital environment for museum management, as well as preventive and predictive maintenance. Furthermore, the study examines—referring to the debate over the widespread application of integrated digital technologies to cultural heritage management—the opportunities and challenges associated with digitization processes towards the implementation of Digital Twin (DT) (Vuoto in Int. J. Arch. Herit. 18(11), 1762–1795, 2024) and Digital Cultural Objects (DCO), as well as the transferability of the study’s findings. The Galleria Borghese Museum provides scholars with the opportunity to examine architecture and artworks integrated into spaces of both permanent and temporary exhibitions, multidisciplinary study areas, restoration spaces for art and architecture, and environments conceived for the valorization, communication, and participation of a large public of experts and non-experts.
Built Heritage Adapted Information Management Through AI. The AIM-EBIM Project
The paper is focused on an ongoing project funded by the Emilia-Romagna region and aimed at the creation of a new workflow finalizing digital data from integrated survey towards an “adaptive” Building Information Modeling (BIM). The project AIM-eBIM—Adapted Information Management for existing Buildings Information Modeling—brings together regional research laboratories and companies to pursue industrial research topics towards a greater deployment of digital tools. Digital surveying has triggered huge potential for innovation, but also generated new challenges in managing and using large amounts of data, often left unused. The quantity of surveyed data used to document built or Cultural Heritage often does not correspond to the quality or reliability of information. Moreover, parametric modeling of existing heritage through BIM is becoming as pervasive as it is necessary, considering regulatory trends. However, these tools can be ineffective from the point of view of users (professionals, companies) who must deal with such complexity. The challenge is to bring discretization (and simplification) processes to source data toward easier informative integration into BIM models, by facilitating and enhancing interpretation needs. In this direction, Artificial Intelligence (AI) algorithms are part of the process. The adapted informative implementation of parametric models is based on digital source data (laser and photogrammetry) segmentation by AI according to specific topics (documentation, analysis, monitoring, conservation, project) and criteria (materials, techniques, components, structures).
Advanced Deviation Analysis Visualization for BIM in Heritage Environment
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.
Automated Scan-To-BIM Methodology for Accurate 3D Modeling of Embedded MEP Systems
This paper presents an integrated methodology to enhance the detection, data acquisition, and modeling of embedded MEP systems in existing buildings, addressing the gap between as-built information and the need for accurate simulation models. The approach begins with a comprehensive digital survey of existing structures using LiDAR and Radar technologies to generate point clouds and accurately tag the coordinates of detected installations. These datasets serve as the foundation for constructing a precise 3D model in an EBIM environment. To automate data integration, a custom API was developed to cross-reference point-cloud coordinates with manual detections, ensuring accurate representation of all embedded systems. The resulting enriched EBIM model is further enhanced by importing it into the Unity game engine. Through the Vuforia augmented reality SDK, an XR experience was created, offering an immersive and interactive visualization of the 3D model and detailed MEP systems. This methodology demonstrates the potential of integrating advanced digital surveying, EBIM, and XR technologies to streamline building surveys and 3D model creation. The proposed approach not only improves accuracy and efficiency but also introduces innovative tools for design, construction, and maintenance workflows.
Semantic Integration of BIM Model with Existing Asset Databases and IoT Data for Public Administrations
Today, information exchange in the AECO industry at different stages of the construction process is typically done through file transfers in heterogeneous formats, with limited communication between parties. This leads to potential data management issues such as redundancy and write errors. While efforts to standardize data exchange date back to the 1990s with formats such as STEP and IFC, the challenge of interoperability remains. That is why it is important to establish integrated management systems and interoperable cloud-based technologies. The rise of open semantic standards by W3C and other organizations in recent decades has been significant, but a cohesive link between different ontologies is needed to realize Digital Twin technology, which represents the lifecycle of a building, not just the design phase. An introduction to the concepts and standards of Semantic Web technologies and their possible applications to the construction sector in the different phases of an asset’s life cycle is proposed. In particular, the paper addresses the management of existing assets by public administrations, focusing on the creation of an infrastructure that links existing traditional databases, BIM models, and dynamic data collected by IoT devices. This will be done using standards such as RDF, OWL, and SPARQL. The research is part of an ongoing NRP project called “BIM2DT. BIM-to-Digital Twin: Information Management to Support Decision-making in the Building Life Cycle”.
Scan-To-BIM-To-VR Processes for the Documentation and Valorization of the Defensive Fortifications in Piombino
The paper intends to explore the integration between Scan-to-BIM parametric modelling techniques and VR virtual systems to support activities of documentation, analysis, valorization, and popular storytelling of Architectural Heritage. In this sense, these themes have been deepened through a series of experimental HBIM applications conducted on the military fortifications of Piombino, and more specifically, on the case study of the defensive complex formed by the Rivellino and the Porta a Terra. Through the implementation of the HBIM model within game-engine platforms, the project aims at enhancing the historical heritage, favouring dissemination and interactive use through immersive virtual environments.
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.
Artificial Intelligence and Extended Reality for Communicating the Uses of Natural Fibers in Building Construction: State of the Art and New Proposals
This contribution presents a digital framework to promote and communicate the use of natural fibers in building construction, developed within the “Circular Design for Natural Fibers” (CD4NF) project. The research addresses the limited application of advanced digital tools in this emerging field by proposing an integrated workflow that synergizes Building Information Modeling (BIM), Artificial Intelligence (AI), and eXtended Reality (XR). The core of the proposal is a flexible and modular platform built upon a structured material filing system that catalogs detailed information on various natural fibers. This platform utilizes BIM as a central data repository. An AI system, based on a Retrieval-Augmented Generation (RAG) model, enables users to query this complex data using natural language. XR applications provide an interface for visualizing and interacting with the BIM models and their associated information in real-world contexts. This approach aims to optimize key processes from material research and design to construction, enhancing decision-making, interoperability, and communication. The system represents a step towards a more efficient, circular, and sustainable construction sector by facilitating the informed adoption of natural fiber-based materials.
Semi-Automated Feature Recognition and Localization in 3D Point Cloud by Using AI
The application of Artificial Intelligence (AI) in Cultural Heritage surveying has gained notable interest, particularly in enhancing the classification and segmentation of 3D point clouds. Recent research focuses on automating processes to manage surveying data and integrating it into the BIM environment. The present research proposes a semi-automated AI-based pipeline to semantically classify architectural features in 3D point clouds and match them with an existing BIM library. The case study examines the school and theatre of the Crespi d’Adda industrial village, a UNESCO Cultural Heritage site. Data includes a 3D point cloud generated via terrestrial photogrammetry, with windows selected as the feature to model in the BIM library. The methodology encompasses three phases: (i) creating a Crespi d’Adda window dataset aligning with BIM library parameters; (ii) applying Machine Learning classifiers for semantic categorization; and (iii) using prediction algorithms to recognize windows in the point cloud, matching them to the BIM library, and calculating accuracy. This research bridges architectural representation and data mining, streamlining BIM reconstruction. Anticipated results include automated detection and labeling of elements with accurate placement in the BIM environment, enhancing efficiency and interdisciplinary integration.
From Temple to Church: The Evolution of San Lorenzo in Miranda Through Machine Learning
The paper addresses the challenges related to the application of machine learning solutions to support historical and architectural critical interpretation. The case study reported here is the complex of San Lorenzo in Miranda, located in the Roman Forum along the Via Sacra. The structure, originally conceived as a temple and later transformed into a church, is a multi-layered architectural palimpsest in which each construction phase, at least since Roman times, has inevitably influenced the subsequent modifications. The building’s rebirth upon itself through the integration and modification of its older portions is its main characteristic, imparting a specific complexity and interest. The first phase of the research focused on bibliographic study and 3D digital survey, both of which contributed to identifying the multiple construction phases of the building. In particular, the digital survey was implemented through two survey campaigns. The first involved a massive 3D laser scanner acquisition and UAV photogrammetric capturing, while the second integrated a topographic survey to georeference the captured data. The second phase concerned data interpretation, focusing on research questions related to a hypothetical reconstruction of the cella’s original wall covering layer. This question was addressed by leveraging machine learning algorithms used to automatically identify the covering traces.