Modular construction uses off-site modules for on-site assembly; computer vision supports progress monitoring, inspection, and assembly. These tasks may occur in poorly lit spaces, where low light degrades image quality, while prior work has not addressed low-light enhancement for this setting. We propose RetinexRefiner, a two-stage framework that first performs illumination-based enhancement and then refines the result to suppress residual artefacts such as colour cast and noise. Trained end-to-end, it achieves improved visibility on night construction images, reaching 28.07 dB PSNR and 0.83 SSIM, and is applicable to construction sites and similar dim environments.
Delays Large metallurgical projects routinely experience severe schedule overruns due to complex, interdependent delay risks that are often recognised only after impacts occur. Conventional risk registers describe potential issues but offer limited guidance on which risks are likely to dominate or interact under specific project contexts. This paper presents a predictive risk analysis framework that anticipates dominant and interacting delay risks before execution. Using limited historical project data, contextual similarity learning, and interaction-based amplification modelling, the framework generates probabilistic completion behaviour and decision-relevant risk prioritisation. Results demonstrate that delay exposure is highly concentrated, context-sensitive, and systemic, enabling earlier, more focused project decision-making under uncertainty.
This paper proposes a topology-driven semantic integration approach to align as-is and to-be BIM models in building renovation scenarios. Starting from independent IFC files, a surface-based topological analysis infers physically meaningful contacts between envelope objects using coplanarity, opposite orientation, vertical overlap, and an overlap-coverage metric. The detected adjacencies are then represented as cross-model semantic relations by instantiating bot:Interface links between the corresponding bot:Element individuals in a dedicated knowledge graph, preserving model independence. The resulting topology enables cross-model SPARQL queries to extract and aggregate energy-related data at the envelopesystem level without IFC merging or manual data preparation.
Requirement verification in integrated contract models, such as Design & Construct, remains a manual, document-centric, and error-prone activity. While technical verification through Automated Code Compliance has advanced significantly, process requirements are typically verified through human inspection of textual documentation. This research addresses this gap by developing a novel artefact architecture that integrates general-purpose Large Language Models, the RASE mark-up language, and Knowledge Graphs to automate traceability between contractual obligations and contractor deliverables. Tested through iterative Design Science Research, the final prototype achieved a precision of 1.000 and accuracy of 0.968, offering a transparent, "white-box" framework for contractual auditability.
Biobased construction materials store atmospheric carbon, yet current life cycle assessment and environmental product declaration practices represent biogenic carbon as aggregated indicators with limited traceability across value chains. This limits transparency, attribution, and credibility of carbon-related claims. This paper explores how blockchain-enabled system principles can address these deficiencies by treating biogenic carbon as a persistent, traceable state linked to material flows. Through an exploratory mapping onto bamboo value chains, we identify how such ledger-based logic can formalize carbon continuity, prevent double counting, and support auditable life cycle transitions. The paper contributes a conceptual framework for carbon ledger approaches in construction.
The Architecture, Engineering, and Construction (AEC) sector faces complex regulatory frameworks like building energy performance certification under the European Energy Performance of Buildings Directive (EPBD). Traditional approaches suffer from fragmented documentation, procedural ambiguities, and poor traceability and auditability, causing outcome inconsistencies and automation challenges. This paper proposes a decoupled conceptual architecture for digitally actionable and traceable regulatory workflows, leveraging compliance modeling, semantic web technologies, and linked data. By separating semantic representation, reasoning, execution, and interaction layers, it improves compliance, interoperability, and adaptability. Demonstrated on Estonian Energy Performance Certificates (EPCs), it addresses key gaps and supports broader application beyond energy efficiency.
The Industry Foundation Classes (IFC) standard underpins BIM interoperability, but its STEP/EXPRESS stack is ill-suited to cloud-native, transactional workflows. Building on buildingSMART community-driven requirements and proposals, review of alternative solutions, and iterative prototyping, we propose the modernisation of IFC. The work includes a proposal for a data schema, JSON serialisation, and exchange mechanisms, while preserving IFC’s role as a semantically rich neutral data model. Innovation includes a compositional, layered federation model allowing incremental updates, multi-author collaboration, versioning, and fine-grained, traceable ownership. We compare the proposal to existing standard and discuss the potential, trade-offs and implications.
This paper presents an automated workflow for generating georeferenced IFC 4.3 models from legacy data in DWA-M 150 XML format, the standard used by German municipal utilities. The approach consists of three stages: geometry generation from master inventory data, semantic enrichment, and integration of inspection and condition assessment data. The resulting semantically enriched models provide a solid foundation for future digital twin applications in sewer infrastructure management.
In wireless structural health monitoring (SHM), the focus is usually placed on reducing the power-consuming data transmission for preserving the limited resources of wireless sensor nodes. In this direction, embedded computing has been leveraged for distributing SHM data analysis tasks to the sensor nodes instead of transmitting raw data to centralized servers. This paper reports on decentralizing SHM tasks using reduced-order numerical models of structures, embedded into wireless sensor nodes as decentralized digital twins that use sensor data to assess the structural condition. The applicability of the proposed approach is demonstrated via laboratory validation tests.
Residential prosumer buildings with PV and high-load subsystems exhibit irregular demand, yet current residential digital-twin and baselining studies rarely address missing-data realism and causal deployment constraints. This paper presents a monitoring-oriented operation-phase digital twin analytics pipeline for an instrumented villa in Segrate, Italy, linking whole-building demand, PV output, pool-circuit power, and outdoor air temperature to a one-hour-ahead baseline task. A fixed Random Forest is evaluated across eight dataset configurations against persistence and Multiple Linear Regression. Under deployment-realistic causal conditions, the best configuration achieves RMSE 576.5 W, demonstrating the value of a traceable leakage-safe baselining workflow for residential prosumer settings.
