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.
Generation and Evaluation of High-Fidelity Digital Twins: An All-Inclusive Pipeline for Enhanced Construction Efficiency in Diverse VR Environments
XR (extended reality) environments have driven professionals in the construction industry to adopt advanced digital surveying and 3D modelling techniques, improving project quality and site management through enhanced visualization, accuracy, and precision. Photogrammetry and laser scanning have been crucial in the scan-to-XR process, enabling the development of digital twins (DT) throughout the construction lifecycle. However, converting survey data into usable models for immersive experiences requires expertise in digital representation and software development. Digital representation transforms raw data into functional models, yet challenges remain. Survey outputs typically feature high polygon counts for detail, which can overload XR applications, causing slowdowns and reducing fluidity, especially on devices with limited resources. Additionally, high-resolution textures further strain computational power and memory. Optimising these textures is key to balancing visual quality and performance. XR platforms like Unity and Unreal Engine demand specific rendering standards, and non-optimised models can fail to meet these, requiring further adjustments. This study aims to develop a pipeline for creating high-fidelity DTs of an ongoing construction site. Data is collected through photogrammetry and laser scanning, followed by model optimisation for XR applications. The optimised model is integrated into real-time platforms for interactive use, producing both VR and web-XR models. This pipeline will aid in construction-site management, safety inspections, and communication between stakeholders, contributing to DT technology and to the efficiency of the construction industry.
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).
Evaluating Urban Perception: Using Explainable Machine Learning Predict Through the Best Pipeline
Understanding subjective urban experiences is essential for designing cities that enhance well-being. Urban design should account for the psychological effects of environments on individuals, as these significantly shape perceptions and behaviors. However, a major challenge is the limited availability of urban perception data. Recent studies have leveraged large, crowdsourced datasets like Place Pulse 2.0 (PP2) to inform machine learning (ML) models for urban perception prediction, but the accuracy and reliability of outcomes remain underexplored. There is a critical need to evaluate whether these datasets truly capture human perceptions. This study investigates the role of urban street images in understanding environmental perceptions, using the PP2 dataset and ML techniques. It explores various ML pipelines, employing TPot AutoML for model selection and 5-fold cross-validation to prevent overfitting. The goal is to identify the most efficient model that strengthens the link between automated predictions and human perception. The study also applies SHAP (SHapley Additive exPlanations) to interpret model outputs, revealing feature importance and interactions. This improves transparency and ensures ML-generated insights are actionable for urban planning. By rigorously testing ML pipelines, this research enhances predictive accuracy and contributes to the development of reliable urban design tools. The findings highlight ML’s potential in processing large-scale perception data, uncovering hidden patterns, and informing people-centered urban planning. However, further validation against real-world surveys is necessary to ensure robustness and generalizability in assessing urban perceptions.
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.
“Campus delle Architetture, del Design e della Pianificazione” Project: WebAR for Public Engagement
The design of the Politecnico di Torino “Campus delle Architetture, del Design e della Pianificazione” has an enormous strategic value, both for the reorganisation of the University and for the city dynamics, contributing to the overall development of the Po cultural axis. The transformation involved by the new campus will significantly impact the life of the area, radically changing its intensity through the daily flows of thousands of people and making the park an actual natural connective tissue. The communication project includes the realisation of events aimed at presenting the Campus to disseminate and popularise the outcomes of the realisation process to different stakeholders, showing a scale model of the area. Among the innovative features of the communication proposal is the superimposition of digital layers on the physical model for audience engagement through a webAR that activates a step-by-step journey with descriptions of different aspects of the campus project.