Conference Agenda

This paper proposes the development of a multi-agent system (MAS) for Building Information Modeling (BIM) environments, where users interact with a 3D model and a chat-bot to query, validate, and analyze building elements. By leveraging conversational AI and modular agents capable of semantic understanding and geometric computation, this system allows users to retrieve data, perform quality checks, and visualize computed results directly using the BIM information. The approach supports diverse tasks, from attribute completion and filtering to volumetric calculations, thus enabling a more intelligent and accessible BIM experience for analytical purposes.

The adoption of innovative digital heritage workflows in the Architecture, Engineering, and Construction (AEC) sector faces significant challenges, particularly in integrating digital survey data with Building Information Modeling (BIM) into a unified model. This paper begins with a literature review that outlines the geometric and software-environment constraints complicating such integration and examines various proposed solutions, with particular attention to open-source tools and standard formats.

Building on this foundation, the paper introduces an innovative two-stage method: (1) segmenting, classifying, and enriching digital survey data into a BIM model; and (2) developing a web viewer that hybridizes this BIM model with the original survey data. The proposed workflow relies exclusively on open-source tools and open standards, with Industry Foundation Classes (IFC) used as the native editing format. A seamless continuity is established between the Bonsai add-on for Blender, used as a BIM authoring environment, and the web library That Open Engine, which serves as a dissemination tool enabling interactive querying of BIM data within a web browser. This library shares a common dependency on Three.js with 3DTilesRendererJS, allowing the overlay of a tiled photomesh of the asset. This integration enables the combination of an accurate geometric and visual representation with structured metadata interaction within a unified web environment. Overall, the proposed approach provides a robust and flexible framework for supporting practical applications such as dissemination, documentation, and diagnostic studies of heritage assets.

This study presents a comprehensive and semi-automated framework for generating multi-Level of Detail (LoD) 3D building models using multi-source LiDAR point clouds to support digital twin development. By integrating airborne, drone-based, mobile, and terrestrial LiDAR platforms, the framework addresses limitations of single-source datasets and enables scalable reconstruction across urban and building scales. A robust preprocessing workflow—encompassing subsampling, denoising, colorization, and two-stage registration—significantly enhances point-cloud quality and achieves seamless fusion of heterogeneous datasets with millimetre-level accuracy.

The framework supports outputs ranging from city-scale footprints (LoD0) to detailed parametric building models (LoD4), enabling applications in smart city planning, facility management, and heritage documentation. A knowledge-based segmentation layer further enables the creation of “Smart Point Clouds,” facilitating component-level querying and efficient generation of floor plans, elevations, and façade models. Real-world evaluations in downtown Toronto demonstrate high accuracy and strong computational performance, with LoD0–LoD2 models produced in minutes on a standard workstation.

By ensuring compatibility with CityGML and IFC standards, the framework enhances interoperability within digital twin ecosystems and supports integration with simulation and decision-support systems. While detailed LoD3–LoD4 modeling still requires manual refinement, the workflow establishes a foundation for future automation through AI-driven segmentation and cloud-based parallel processing. Overall, this research advances scalable 3D modeling practices and provides a practical pathway toward comprehensive, data-rich digital twins for smart cities.

With the Czech Republic's impending "BIM Act" driving the creation of a basic built environment model, the study proposes a compliant workflow for incorporating 3D models of two key vegetation feature types from the fundamental geographic vector database: "Forest ground with trees" and "Significant or lonely tree, grove." Modelling relies on nationwide datasets, the digital terrain model, the digital surface model based on image matching of aerial imagery, and supplementary aerial laser scanning data. For the forest features, the process comprised optical point cloud filtration and constrained triangulation, resulting in height-extruded forest base polygons with canopy cover tops. The 3D representation uses MultiSurface geometry, recorded as a PlantCover object in CityGML/3DCityDB, and is in line with the LoD2 standard for buildings. For solitary trees, predefined prototypes were scaled and positioned based on individual tree detection and parameters extracted from point clouds. Features were mapped to the CityGML/3DCityDB SolitaryVegetationObjects class, utilizing Implicit geometry to optimize for data volume and visualization speed. While the digital surface model, which can be easily generated from periodically acquired optical imagery, was sufficient for the forest features, aerial laser scanning data was superior in individual tree modelling. The number of extractable parameters increases with point density and is dependent on the platform used. However, the availability of such higher-density laser scanning data in Europe is limited and varies across countries and regions. The results demonstrate the generation of LoD2 compliant 3D models from nationwide datasets for both vegetation features, visually enriching the basic built environment model.

Despite the rapidly evolving and widely adopted tools in the Architecture, Engineering, Construction, and Operations (AECO) industry, Construction Supply Chain Management (CSCM) remains a fragmented practice with poor integration and interoperability between Building Information Modelling (BIM), Geographical Information Systems (GIS), and logistics systems. This research aims to bridge the gap between BIM, GIS, and logistics information by developing a unified, data-informed Digital Twins (DT) framework necessary to support multi-criteria decision-making (MCDM) in CSCM. They key characteristics of this work include: (1) a repeatable integration for heterogenous BIM-GIS environments powered by IoT networks; (2) a short-horizon predictive module optimized for construction logistics and Just-in-Time (JIT) delivery; and (3) a democratized analytics interface.