3D concrete printing (3DCP) has reached early industrial deployment, yet current standards exclude environmental, health, and safety aspects of robotic operation, and existing collaborative-robot frameworks do not address process-coupled hazards or multi-role construction environments. This paper proposes a three-layer taxonomy (Human, Robot, Site) and a site-wide predictive digital twin with five-second update cadence and short-horizon forecasting. The framework is operationalized through seven role-aware interaction patterns aligned with ISO 10218. A scenario-based evaluation of the Human-layer physiological pipeline using a proxy dataset demonstrates the feasibility of stress classification and calibration. The work provides a reference architecture for human–robot–site integration in 3DCP.
Students are often challenged to apply core building energy concepts in modelling workflows where results depend on in-depth simulation know-how and experience. The paper presents a novel curriculum design for a masters level building energy module delivered through a combined lecture and project format and assessed through two summative components. Cohort evidence includes a pre and post comparison of project outcomes between 2014-2021 and 2022-2025. Project outcomes show similar central tendency pre versus post periods, with reduced spread in the post period. The paper provides a transferable module structure, workflow, and assessment rubric for building energy education.
Building operations account for major global energy use, yet decarbonisation is hindered by fragmented data silos. This paper presents a utility-aware knowledge graph developed for multi-scale residential energy modelling. By leveraging Linked Data principles and the DDIM server, the framework integrates building-level performance data with national electricity distribution networks. Using Ireland as a case study, the methodology semantically associates 2.3 million residences with over 46,000 low-voltage transformers through a novel enrichment process. This scalable approach enables planners to perform complex queries for grid capacity analysis, bridging the gap between individual building physics and urban-scale energy simulation.
Infrastructure digital twins are transformative tools for managing complex systems. This study examines how infrastructure digital twins are narratively constructed and how these narratives shape governance. Drawing on narrative theory, we analyze 75 academic publications and their framing of digital twins to identify recurring conceptual patterns. The findings indicate that digital twins are beyond mere technical artefacts, rather are embedded in narratives that implicitly script technological function, institutional roles, and governance alignments. There is a need to treat digital twin adoption as a technical innovation and also as an institutional design challenge shaping how infrastructure decisions will be made.
Conventional element-centric BIM-LCA integration omits temporary works and faces semantic barriers, causing significant carbon underreporting. We propose a dynamic ontology-driven framework to overcome this. Its semantic core is a novel System-Element-Process-Resource-Carbon ontology, shifting assessment from static elements to context-aware construction processes. An automated pipeline transforms OpenBIM data into a carbon knowledge graph, capturing permanent and temporary emissions. A neuro-symbolic multi-agent system enables natural language querying and scenario simulation for interactive decision support. The framework ensures more complete and automated carbon accounting and establishes a semantic foundation for data continuity across the project lifecycle.
This paper addresses the tension between blockchain’s technical potential and the need for legal compliance in highly regulated property markets. Drawing on Institutional Cryptoeconomics, we propose a hybrid governance architecture for real estate transactions in Belgium that complements, rather than replaces, existing legal frameworks. The system combines smart contracts for automated escrow and settlement, an AI-based assistant to reduce ex ante coordination costs, and civil notaries as institutional oracles for legal validation. By embedding algorithms within mandated structures, this “trustworthy” governance model aims to reduce transaction costs while ensuring regulatory alignment, offering a scalable template for adoption in real estate.
