The complexity of building codes and variations in Building Information Modelling (BIM) practices have long challenged Automated Compliance Checking (ACC). The success of Large Language Models (LLMs) in code generation opens new opportunities for this domain. Unlike previous work relying on machine-readable rules or LLMs using hard-coded tools that limit scalability, this paper examines whether LLMs, using the Code-Act pattern, can generate scalable compliance logic for regulations up to Solihin and Eastman's complexity class 3. We show that iterative refinement with a strong verifier evolves reusable checking functions for consistently modelled BIM data, while geometric reasoning requiring auxiliary constructions remains challenging. Source code and data available at: url{https://github.com/stefan-1992/ACC-function-generation.
Contemporary practices like evidence-based circularity and material tracking and tracing, are characterized by collaborative processes to layer, recombine, and update information non-destructively. This paper explores how the emerging IFC5, with USD-inspired, layer-based composition, enables such needs. The data model consists of layers, representing geometry, material characteristics, and lifecycle evidence. We explain how the principles help in a social housing circular window reuse scenario using a python-based web application on top of a Postgres database. By structuring information as composable layers, the introduced data model supports evidence-based circularity tracking, allows material and component matchmaking and information management.
Digital Twins (DTs) are promoted as a transformative paradigm for the built Environment, yet their value is articulated through benefit propositions that lack cost integration. This study proposes a new conceptualisation of DTs value creation for asset owners, based on System Dynamics (SD). DT costs and benefits are identified first and modelled as variables of four sub-systems representing operational, human, financial and market mechanisms across the asset lifecycle. The results reveal different feedback loops of how value is realised. The contribution is a general system-level explanation of DT value that supports more informed investment decisions.
While modularization techniques for MEP project can reduce on-site work and improve efficiency, existing installation optimization approaches focus on static constraints and lack robust consideration of dynamic interactions in the on-site assembly process. Addressing these challenges, this paper develops an MEP installation optimization framework integrating DSM-based module division and feedback-driven 4D simulation, which extracts IFC data for dynamic modeling, clusters elements into transportable modules, and employs a closed-loop algorithm to optimize installation sequences. Validated on a real-world project, the system successfully generates collision-free kinematic paths and significantly reduces installation costs, thereby enhancing both the efficiency and constructability of MEP.
Design changes during construction remain a challenge, as late-stage modifications to complex MEP systems can impact cost and schedule. While BIM enables documentation of design evolution through versioned IFC files, existing tools focus on retrospective change detection rather than forward-looking stability assessment. This study introduces two metrics, Change Intensity (CI) and Stability Index (SI),to track MEP design evolution across consecutive IFC versions.CI aggregates the proportion of elements changed, while SI applies a logistic function to translate CI into an interpretable stability score. Eleven IFCs from a real project demonstrate the framework’s ability to identify disruption events and track design convergence.
Construction progress monitoring requires integrating daily progress reports (DPRs) with baseline schedules for productivity analysis and risk management. Manual integration is unscalable due to heterogeneous formats and inconsistent terminologies. This paper presents an automated system for enriching the schedules from the DPR files utilizing the large language models (neural) and knowledge graphs (symbolic) in a bidirectional integration, achieving the neuro-symbolic capability. It learns construction terminology from the schedule and the DPRs automatically, adapts its confidence weighting as domain knowledge matures, and validates against temporal, resource, spatial, and causal constraints. Tested on a real construction project with 173 DPR files and 1000 validation activities, results show a median confidence of 0.825, with zero constraint violations. It demonstrated substantial growth in the knowledge graph and greater robustness than pure neural and symbolic baselines. This neuro-symbolic integration offers a practical and interpretable solution for scalable schedule enrichment in real construction environments.
Decentralized digital product passports (DPPs) retrieval via data spaces are emerging as a promising approach to enable material reuse in the Architecture, Engineering, Construction, and Operations (AECO) industry. Yet, no DPP-specific operational data space currently exists, and usage policy implementation remains unexplored. Building on Eclipse, this paper develops a proof-of-concept DPP data space, implementing asset registration, usage policy definition, contract negotiation, and authenticated data retrieval. Demonstrated through a suspended ceiling element, the research shows how multiple data providers can offer modular DPP data under differentiated usage policies, offering the first technical validation for decentralized DPP exchange in the AECO industry.
Despite growing digitalization of energy management systems (EMS) in buildings and local networks, integrating data-driven monitoring and control remains challenging. Edge computing offers enhanced local processing that can be embedded in existing EMS infrastructures. Within the European HYSTORE project, two edge-computing use cases were developed: a network-level platform enabling coordinated EMS operation using building thermal mass estimates, and a building-level controller supporting advanced monitoring and control of thermal energy storage. Both architectures are evaluated for design, integration, and potential for federated learning, enabling secure, efficient, and privacy-preserving multi-agent control.
This paper proposes a three-stage workflow to prioritise reusable building product groups in Dutch demolition outflows, addressing the gap between impact potential and implementation feasibility. Stage 1 screens 139 groups using an impact proxy combining mass outflow and the environmental cost indicator, shortlisting ten candidates. Stage 2 applies a five-criteria multi-criteria analysis with weights from eight experts using the Best–Worst Method, where supply–demand balance dominates (0.296) and sustainability ranks lowest (0.137). Stage 3 links ranking to barrier diagnosis through a masonry case, identifying contamination, certification, and coordination challenges. The workflow supports identifying high priority product groups for early-stage reuse planning.
Inner-city construction projects face severe spatial constraints during the finishing stage, often leading to mixed construction and demolition waste (C&DW) handling and reduced circular performance. This paper proposes a project-level decision-support framework to evaluate outbound C&DW logistics strategies, addressing the lack of project-level comparative tools. The framework integrates economic, environmental, and social indicators in a scenario-based comparison of conventional handling and CCC-enabled reverse logistics. Applied to three Dutch projects, results show cost and emission reductions, with slightly higher logistics intensity. The framework supports ex ante decision-making under spatial constraints.
