Conference Agenda

Artificial Intelligence (AI) integration has become an essential of modern AEC workflows, yet it has failed to gain a position in waste management. This gap is particularly prominent given the urgent environmental and legal imperatives for the sector to mitigate its demolition outputs. Existing approaches to waste classification and diversion cost estimation rely on manual interpretation of project documentation, a process that is both resource-intensive and structurally incompatible with the machine-readable data environments established by Building Information Modelling (BIM). This paper presents a framework that bridges Industry Foundation Class (IFC) compliant BIM data and Large Language Model (LLM) capabilities to automate Construction and Demolition Waste (C&DW) classification and probabilistic cost optimisation. The framework utilizes IfcOpenShell to extract element geometry and material data, channeling this information into a Retrieval-Augmented Generation (RAG) pipeline. To ensure rigorous compliance during classification, a FAISS-indexed knowledge base grounds a locally deployed Llama3 model against the specific mandates of Province of Ontario, Canada regulation 102/94. Diversion cost scenarios are computed through a Bayesian cost module coupled to a multi-objective genetic algorithm (MOGA) optimiser. Th proposed approach is evaluated against a labelled dataset of 104 IFC type-and-material combinations, the RAG classifier. Performance thresholds were established a piori based on multi-class classification benchmarks and Bayesian cost model uncertainty tolerances. The framework achieved a macro-average F1 of 0.84 and overall accuracy of 88%, satisfying the minimum criteria for automated C&DW characterization under Ontario Regulation 102/94.

This case study details the integration of official large-scale open 2D and 3D geospatial data of the city of Berlin, Germany, into the Virtual Battlespace 4 (VBS4) simulator for security applications. Realistic scenery with elements specific to the target area is obtained from a digital terrain model, true-ortho mosaic, and high-resolution land use/land cover layer rasterized from OpenStreetMap vector primitives. For the central Mitte borough with its government institutions and foreign embassies, almost 20000 buildings are prepared from textured CityGML data in an automatic multi-stage process. This process involves pre-wrapping the texture images, which are referenced by the semantic 3D models using non-canonical coordinates, and the rapid creation of compact atlases to reduce the bitmap count by three orders of magnitude. To ensure that the building meshes blend seamlessly into the terrain, vertical adjustment methods are discussed, and ground extrusion is implemented to approach the model's base surfaces from below. Data import into VBS4 happens through its Geo interface for the terrain, ortho, and land cover, while the buildings are compiled into an add-on with a custom workflow that involves reprojection, collision component setup, and damage behavior configuration. During interactive convoy training in the virtual environment, a high recognition value compared to the real landscape could be attested visually. Simulation exhibited acceptable frame rates, but required considerable computing resources.

Water scarcity and increasing demand have made sustainable water management a global priority, reflected in UN SDG 6, which emphasizes water-use efficiency and reducing water scarcity. Smart Water Management (SWM) has emerged as an advanced, data-driven approach that leverages ICT and IoT systems to monitor, analyze, and optimize water use. Digital Twin (DT) technology enhances SWM by creating dynamic virtual replicas of physical systems to support predictive analytics and operational intelligence. While DTs are widely used in large-scale Water Distribution Networks, these implementations typically do not require detailed 3D modelling.

Campus-scale water systems present unique challenges due to the integration of external and interior water networks, variable building functions, and the need for detailed spatial representation. This study proposes a comprehensive DT framework for Smart Water Management at Toronto Metropolitan University. It integrates BIM, GIS, sensor data, and graph-based modelling to capture 3D interior utilities and enable real-time monitoring, hydraulic simulation, and network analysis. The framework adopts Tao et al.’s five-layer DT architecture and introduces the IFCGraph Model, which combines IFC multipatch geometry with a Neo4j knowledge graph for enhanced interoperability and topological analysis. Overall, the framework supports operational intelligence, proactive management, and scalable campus-level water system optimization.

Urban Digital Twins (UDTs) are increasingly recognized as enablers of evidence-based planning and citizen engagement. While the involvement of civil society in planning the built environment is well established, its role and motivation in advancing the clean energy transition remain largely unexplored. This paper presents the development and application of an Open Geospatial Consortium (OGC) standards-based UDT platform for the co-creation of Positive Energy Districts (PEDs), as demonstrated through the Nordbahnhof district case study in Stuttgart. The platform integrates interoperable 3D city and energy data using CityGML 2.0 with its Energy ADE 3.0 extension, both compliant with OGC standards to ensure semantic consistency and cross-domain interoperability. SimStadt energy simulation results are stored in the Energy ADE schema within PostgreSQL/3DCityDB database. These data are published through an OGC Web Feature Service (WFS), while 3D city geometries are served as 3D Tiles. In the CesiumJS web-viewer, both services are linked via GML identifiers, enabling coordinated interaction between geometry and energy data for real-time visualization of the district-scale energy balance. The platform was tested with citizens, who learned about load profiles, photovoltaic (PV) potential, and energy efficiency while acting as “district energy planners.” Their responses/willingness to adopt PV and/or modify energy-use behavior were translated into slider inputs to visualize real-time energy-balance outcomes through the platform. Results demonstrate the potential of interoperable, OGC-compliant UDTs to connect data providers, planners, and citizens in a shared decision-support environment. The architecture’s open, modular design enables wider replication, promoting scalability and long-term municipal adoption for participatory energy-transition planning.

Building Information Modeling (BIM) is increasingly mandated worldwide as part of the digital transformation of the construction industry. While widely used in design and construction, its potential for managing construction and demolition waste (C&DW) remains underexplored, despite demolition accounting for 70–90% of building-related waste and 30–40% of global solid waste. Revit models provide rich data but are computationally intensive and require specialist expertise, limiting their direct use for waste quantification and sustainability evaluation. This study develops a BIM-enabled data integration and visualization framework that automates waste estimation, material classification, and environmental evaluation by linking BIM data with heterogeneous datasets through Speckle connectors and Power BI dashboards. Supplementary datasets included material densities, expansion coefficients, recycling rates, and environmental factors such as CO₂ emissions and energy intensities. A case study of York University’s Bergeron Centre illustrates the framework’s effectiveness across three demolition stages. The non-invasive dismantling phase highlighted significant opportunities for material recovery, while semi-invasive deconstruction captured recyclable structural components with moderate landfill requirements. The final core demolition stage revealed the greatest potential for recycling, particularly in concrete and steel, though it also underscored the challenges of diverting large volumes of residual waste from disposal. By integrating BIM with environmental datasets and interactive dashboards, the system delivered holistic insights into recovery, landfill diversion, and CO₂ reduction. Findings confirm its scalability, accessibility, and value as a decision-support tool for sustainable demolition and circular economy objectives.

This contribution presents a prototype Earth Observation-based simulation framework to assess how large-scale urban interventions affect Land Surface Temperature (LST). Focusing on the Metropolitan City of Milan (Northern Italy), the framework integrates thermal (Landsat 8/9) and multispectral (Sentinel-2) satellite imagery with Local Climate Zone (LCZ) maps, urban morphology and material fraction layers. Random Forest regression models are trained to predict seasonal LST patterns. A simulation module, based on raster algebra, enables scenario testing by modifying predictor layers to reflect planned urban transformations, generating corresponding LST responses. The framework is conceived for integration into Urban Digital Twin platforms to support “what-if” scenario analyses for climate-resilient urban planning and adaptation.