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.
Building energy simulation is a key competence in energy systems engineering education. Students are often challenged to appropriately apply core concepts in modelling workflows. This paper presents a novel curriculum design for masters level building energy module delivered through a combined lecture and project format. Cohort evidence includes pre and post comparison of project outcomes. Project outcomes show similar central tendency pre versus post periods, with reduced spread in the post period, consistent with more structured milestone-driven delivery. The paper provides a transferable module structure, workflow, and assessment rubric for building energy education using professional modelling toolchains.
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.
The digital transformation of structural inspection has led to the widespread use of image-based artificial intelligence for automated damage detection. Deep learning models, particularly convolutional neural networks, have demonstrated strong capability in identifying surface-level defects such as cracks from photographic data. However, although these systems provide pixel-level predictions, they lack mechanisms for semantic interpretation, crossdomain data integration, and structured knowledge representation. For deployment in civil engineering workflows, AI-based inspection must move beyond detection toward contextual reasoning and interoperable knowledge capturing.
The construction industry uses BIM and Common Data Environments (CDE) for coordination, nevertheless struggles with data integrity, auditability, and trusted verification for safety compliance amid fragmented processes and BIM-blockchain oracle issues. This study proposes a hybrid framework integrating BIM, DynamoPython automation for parameter extraction and data validation, cryptographic hashing, and QR-codes linking physical assets to blockchain-ready records. In a Rome heritage refurbishment case study on mobile scaffolding, it produced verifiable inspection registers with compliance status and traceable hashes. The key contribution is a scalable prototype enhancing BIM safety monitoring traceability, enabling blockchain anchoring and openBIM-aligned digital twin provenance.
Anomaly detection in building sensor networks often relies on static thresholds that fail to capture temporal dependencies and system changes. This work combines a baseline model of expected indoor environmental behavior with anomaly detection to form a data-driven Digital Twin. In this framework, a regression model learns expected behavior from historical data, while deviations are quantified as multivariate residuals and analyzed using unsupervised and semi-supervised methods. Preliminary analysis on a representative campus sensor shows detection of anomalous behavior and associated distributional shifts, with sensor relocation serving as an illustrative system change.
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.
Scotland’s construction industry operates at a circularity rate of just 1.3%, despite construction accounting for approximately 50% of all material consumption. The European Union’s Construction Products Regulation and the Ecodesign for Sustainable Products Regulation mandate Digital Product Passports (DPPs) for construction materials, assuming linear supply chains, with no governance model for reclaimed materials re-entering circulation. This paper addresses this gap by applying Ostrom’s principles for commons governance to a blockchain-based digital marketplace for construction material reuse. We employ hybrid methods to propose a prototype architecture of seven smart contracts designed for EU DPP compliance
