Optimizing multi-purpose specifications requires understanding information requirement overlap and burden distribution, yet systematic object-level analysis remains absent. This study introduces a hierarchical 12-KPI framework quantifying geometrical, alphanumerical information, and documentation requirements across 80 objects, 22 purposes, and five lifecycle phases using 5,048 specifications. The key contributions include: (1) a validated three-archetype hierarchy with distinct temporal signatures; (2) the identification of universal objects as multi-purpose integration points; (3) the quantification of purpose-specific uniqueness challenging consolidation assumptions; (4) an interactive dashboard enabling evidence-based Exchange Information Requirement optimization. This object-purpose-phase granularity advances systematic BIM specification from aggregate analysis to decision-support frameworks for practitioners.
CityRetroFit integrates publicly available datasets with building archetypes to create urban building energy models (UBEMs). The platform integrates geospatial data, occupancy schedules, envelope constructions, and HVAC system characterizations for both baseline and potential retrofit scenarios and supports retrofit prioritization at scale. This paper summarizes the development of this workflow in a mandatory building energy disclosure context () and its extension to jurisdictions without such disclosure (). A case study of is presented to demonstrate the workflow and its validation with aggregate energy data. The results show CityRetrofit’s adaptability across jurisdictions to inform urban decarbonization policy.
Uncertainty analysis is critical for reliable building energy predictions, yet classical Monte Carlo simulation is computationally prohibitive for detailed physics-based models. This study proposes a BIM-integrated adaptive surrogate uncertainty analysis framework combining adaptive sampling with sparse Polynomial Chaos Expansion to efficiently propagate uncertainty and perform global sensitivity analysis. The approach is demonstrated on four Irish residential archetypes with uncertain envelope thermal parameters. The adaptive surrogates achieve stable accuracy (CV-RMSE 0.74–2.05 kWh/m² for annual EUI) while reducing computational cost by around 99% compared to Monte Carlo simulation, enabling scalable and interpretable decision support for building design and retrofit.
Building handover is a critical stage in the building lifecycle, yet project information is often fragmented, difficult to verify, and hard to reuse in later operation and maintenance. Digital Twins (DTs) can improve lifecycle visibility, while BC can strengthen trust and traceability(Dounas et al., 2020). However, current studies rarely combine these with semantic knowledge representation. This work proposes a Blockchain-enabled semantic DT framework for building handover, using a five-layer architecture and a knowledge graph to structure building information. A lightweight prototype demonstrates trusted data admission and auditable event recording. The contribution is a practical and reproducible framework for improving lifecycle continuity beyond handover.
The building sector is undergoing a twin transition in which digital and sustainable transformation must be addressed jointly. Managing this transformation requires professionals equipped with green and digital competences. Existing research highlights the relevance of these skills, their systematic integration in education and professional practice remains limited. This paper addresses this gap through a qualitative case study based on 53 expert interviews and an expert workshop conducted in a German metropolitan region. The findings reveal significant green and digital skills gaps. Based on that, the study contributes an integrated framework for strengthening these competences across higher education and professional practice.
Digitising existing buildings supports energy-efficient retrofitting and lifecycle analysis, yet many are documented only by legacy drawings with dimensions embedded as textual annotations. Existing methods often rely on heuristic global scaling over metric fidelity. This paper presents a vision-based framework integrating optical character recognition (OCR), OCR-guided denoising, semantic segmentation, and grid-based alignment. The primary contribution is a cross-modal text–geometry alignment strategy that reconstructs a metric reference grid from annotations and links it to building geometry. A second contribution is an Industry Foundation Classes-based evaluation framework. Results demonstrate reliable recovery of absolute dimensions for dimension-aware BIM reconstruction.
BIM-based cost estimation often relies on proprietary solutions and ad-hoc mappings to external cost databases, limiting interoperability and auditability. This paper presents an openBIM workflow that enriches material definitions with standardized Canadian-specific cost items and automatically generates element-level quantity takeoff and capital cost breakdowns. A case study demonstrates automated cost extraction with material, labor, and equipment components, plus structured inputs for a maintenance-and-replacement-oriented life-cycle cost view. The approach automatically enriches BIM models with cost information, links quantities to standardized cost data, and provides life-cycle cost estimates in accordance with the openBIM interoperability standard.
Off-site construction relocates building processes to controlled manufacturing environments, enhancing productivity but increasing the risk of cost overruns due to the non-repetitive nature of module fabrication. This work presents a machine learning benchmark for a cyber-physical system that digitises manufacturing operations by capturing machine working times alongside design and production features. The benchmark spans tree-based and support vector regressors, with the best-performing model embedded in an industrial dashboard for data-driven scheduling. Validated in a UK steel frame manufacturer across eight months of production data, Random Forest achieved 10.84% median absolute percentage error on 420 frames, enhancing production planning and scheduling.
The geospatial surveying sector is undergoing rapid digital transformation, intensifying demand for advanced digital skills amid widespread workforce shortages and declining education pathways. Micro credentials have emerged as a flexible solution for targeted, industry aligned upskilling, yet their effective development requires structured collaboration between Higher Education Institutions and industry. This paper proposes a conceptual framework for designing Digital Geospatial Micro Credentials through an Industry–Academia Collaboration model, synthesising multidisciplinary literature and preliminary action research. The framework integrates key drivers, collaborative patterns, and outputs, offering a foundation for future validation and enabling responsive approaches to developing digitally skilled geospatial professionals.
Tracing information related to the life cycle of building products supports decision-making for circular construction. However, current digital tools and standards in construction primarily address product identification at the type rather than instance level, resulting in information loss that hinders reuse. To trace products across projects at the instance level, we propose a graph-based workflow that maps globally unique identifiers (GUIDs) of element instances. While information continuity can be established manually, preliminary workflow evaluation in a case study reveals that manual GUID verification is impractical. We conclude that realising a digital reuse supply chain requires software automation for instance-level identification.
