In the last years, the AEC (Architecture, Engineering and Construction) domain has exponentially increased the use of BIM and HBIM models for several applications, such as planning renovation and restoration, building maintenance, cost managing, or structural/energetic retrofit design. However, obtaining detailed as-built BIM models is a demanding and time-consuming process. Especially in historical contexts, many different and complex architectural elements need to be carefully and manually modelled. Meshes or surfaces and NURBS or polylines, derived from 3D reality-based data, are recently used as a reference for the HBIM accurate modelling. This work proposes a comprehensive and novel semi-automated approach to reconstruct architectural elements through the Visual Programming Language (VPL) Dynamo software and a Boundary-Representation method (B-rep), starting from 3D surveying data and point clouds classification. A wide package of scripts provides solutions for modelling complex shapes and transferring the obtained 3D models into BIM Authoring tools for a complete reconstruction phase. The presented procedure, useful for different BIM or HBIM applications, proved to reduce the modelling time significantly.

In recent years, the improvement of sensors and methodologies for 3D reality-based surveying has exponentially enhanced the possibility of creating digital replicas of the real world. LiDAR technologies and photogrammetry are currently standard approaches for collecting 3D geometric information of indoor and outdoor environments at different scales. This information can potentially be part of a broader processing workflow that, starting from 3D surveyed data and through Building Information Models (BIM) generation, leads to more complex analyses of buildings’ features and behavior (Figure 1). However, creating BIM models, especially of historic and heritage assets (HBIM), is still resource-intensive and time-consuming due to the manual efforts required for data creation and enrichment. Improve 3D data processing, interoperability, and the automation of the BIM generation process are some of the trending research topics, and benchmark datasets are extremely helpful in evaluating newly developed algorithms and methodologies for these scopes. This paper introduces the InCUBE dataset, resulting from the activities of the recently funded EU InCUBE project, focused on unlocking the EU building renovation through integrated strategies and processes for efficient built-environment management (including the use of innovative renewable energy technologies and digitalization). The set of data collects raw and processed data produced for the Italian demo site in the Santa Chiara district of Trento (Italy). The diversity of the shared data enables multiple possible uses, investigations and developments, and some of them are presented in this contribution.

In response to the increasing demand for effective bridge management and the shortcomings of current proprietary solutions, this work presents an open-source, web-based platform designed to support bridge inspection and data management, particularly for small and medium-sized public administrations, which often lack personnel or funding for implementing context-specific tools. The system addresses fragmented workflows by integrating multi-format geospatial and 3D data—such as point clouds, CAD/BIM models, and georeferenced imagery—within a unified, modular architecture. The platform enables structured inventory, interactive 2D/3D visualization, defect annotation, and role-based user interaction, aligning with FAIR principles and interoperability standards. Built entirely with free and open-source tools, the P.O.N.T.I. prototype ensures scalability, transparency, and adaptability. A multi-layer navigation interface guides users through asset exploration, inspection history, and immersive 3D viewers. Fully documented and publicly available on GitHub, the system allows for deployment across varying institutional contexts. The platform’s design anticipates future developments, including integration with IoT monitoring systems, AI-driven inspection tools, and chatbot interfaces for natural language querying. By overcoming existing proprietary limitations and providing access to a versatile single space, the proposed solution supports decision-makers in the digital transition towards a more accessible, transparent and integrated infrastructure asset management.

The creation of Building Information Models (BIM) is driven by cutting-edge software applications, plug-ins, and APIs that constitute the backbone of BIM authoring tools. While free tools and APIs offer visualization and customization options, geometric modelling remains largely restricted to interactive work and proprietary platforms, which sometimes limits flexibility and efficiency. There are still only a few comprehensive workflows that fully automate the reconstruction of building elements from reality-based surveyed data. This paper introduces an innovative reconstruction pipeline developed for the Scan-to-BIM Challenge at the CVPR 2024 Workshop, where it achieved second place in the competition. A deep learning (DL)-driven BIM Module for parametric IFC reconstruction is designed to accurately reconstruct both primary and secondary building elements within a BIM framework, starting from unstructured point cloud data captured via Terrestrial Laser Scanning (TLS). By leveraging DL techniques, particularly Convolutional Neural Networks (CNNs) and Transformers Networks (PTv3), our approach uses late fusion instance segmentation across both 2D and 3D modalities to accurately identify and reconstruct class-specific elements. The pipeline ultimately generates Industry Foundation Classes (IFC) elements, enhancing modelling accuracy, parameter estimation, and consistency in subsequent stages. Results highlight the pipeline’s strong performance on various datasets, underscoring the crucial role of DL in advancing Scan-to-BIM workflows.

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the Architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations.

Building renovation to improve energy efficiency is crucial for reducing CO₂ emissions, aligning with the goal of achieving net-zero emissions by 2050. This task requires a holistic approach that encompasses retrofitting outdated systems, enhancing thermal insulation, and integrating renewable energy sources. Simulating different indoor environmental conditions and technological systems within Digital Twin (DT) before interventions is crucial for optimizing energy efficiency. Simulations can support the proper installation of heating and cooling devices and facilitate the deployment of advanced technologies, including smart Heating, Ventilation, and Air Conditioning (HVAC) systems, energy-efficient lighting, and automated energy management solutions. The use of Artificial Intelligence (AI) in simulations allows for the precise sizing of HVAC systems, including heat pumps and related devices, by accurately modelling demand profiles and optimizing sensor placement based on the geometries of DTs. This study, conducted as part of the Horizon Europe InCUBE project1, explores a real-world use-case at the Centro Servizi Culturali Santa Chiara in Trento, Italy. It introduces an innovative approach that integrates 3D surveying, computational fluid dynamics (CFD), and digital twin (DT) geometries to enhance the analysis of indoor heat distribution. The proposed data-driven pipeline optimizes sensor placement within indoor spaces, ensuring precise system design, improving performance and energy efficiency, and minimizing energy waste while preventing the oversizing of technological systems.