The construction sector's current sustainability frameworks are centered around private buildings, with little consideration for public infrastructure. Therefore, this paper reviews global rating frameworks to address these gaps and examines 629 indicators grouped into quantitative and qualitative categories. To understand how sustainability and resilience indicators relate to one another, Social Network Analysis (SNA) was used, and 113 distinct indicator groups were identified, with a high modularity of 0.975, suggesting weak cohesion among the frameworks and a fragmented approach to evaluations. Thus, a three-level hierarchical framework is developed to select and make decisions about indicators for sustainable public infrastructure in India.
The integration of Digital Building Permits (DBP) and Digital Building Logbooks (DBL) is increasingly recognised as essential for sustainable and data-driven construction and building lifecycle management. This study examines how European initiatives, including permitting platforms, logbook gateways, city digital twins and building stock observatories, contribute to DBP and DBL integration, often implicitly. Using a step-by-step thematic analysis of stakeholder and expert interviews, the research identifies shared data foundations, maturity gaps and governance challenges. The findings outline initial pathways for aligning fragmented initiatives and support the development of coherent and interoperable DBP and DBL ecosystems.
This paper proposes a robust Variational Physics-Informed Neural Network (VPINN) framework specifically tailored for the structural analysis of thin Kirchhoff-Love plates. We utilize Approximate Distance Functions (ADFs) for hard boundary constraints and a hybrid spectral variational formulation to overcome challenges associated with fourth-order partial differential equations, such as vanishing gradients and the need for C1-continuous meshing. Our VPINN framework can learn a parametric design space and enable real-time, mesh-free structural analysis, eliminating the ‘‘modeling-meshing-solving’’ cycle. The results confirm that our VPINN accurately captures critical stiffness trends and global deformation modes, satisfying the precision requirements for conceptual design and topology optimization.
Incremental renovation creates uncertain inputs for archetype-based UBEM, especially in Soviet-era apartment blocks with incomplete renovation records. This study evaluated whether heating demand can indicate renovation depth (none/partial/deep) for 44 buildings in Mustamäe, Tallinn (464A and 317A series). Using the REST tool, this study simulated typical single and combined envelope upgrades and derived the lowest and highest heating-demand ranges. Heating demand decreases consistently with renovation depth and isolates deep renovation, but several partial renovation states overlap. Tier thresholds differ by building series, and a best-case sensitivity test confirms the robustness of these patterns.
Construction stakeholders refer to the same asset using discipline-specific terms and abstraction levels, producing representations with inconsistent naming conventions and modeling granularity. Alignment across views often fails even with standards such as IFC, unless semantic relations and transformations are explicit. We contribute a typology of cross-view mapping requirements as a preceding decision layer for alignment. It determines the mapping type, intended semantic relation, and required transformation between two views, guiding candidate generation and validation. The typology covers direct entity correspondence, attribute-to-entity transformation, and cluster-based mapping, illustrated with examples from planning, construction, and operations, showing why label matching alone is insufficient.
Construction projects, which are prone to delays and budget overruns, struggle to transform fragmented data streams into coherent intelligence. We propose a novel approach to monitoring that combines onsite data capture, agentic workflows and knowledge graph architecture to provide a foundation for evidence-based progress tracking. This approach is built on an ontology for structured schedule management that unifies concepts from domain-specific ontologies and Autodesk Construction Cloud. Autonomous AI-driven agents enable knowledge evolution by interacting with a knowledge graph. This lays the foundation for a living digital twin that adapts as new data emerges, supporting consistency checking and cross-system integration.
Preventive seismic safety is a crucial topic in diverse
territorial contexts, at national level, in Italy a major issue
is represented by the central and south Italy, however in
some areas in the north of Italy the seismic zoning has
been updated increasing the level of safety of the areas
and introducing a higher level of risk in the east part of
the northern Italy.The paper presents a
BIM-based Diagnostic Digital Twin framework
supporting seismic vulnerability assessment, intervention
design, and construction-phase safety management for
occupied school buildings.
Automated Compliance Checking (ACC) is a longstanding challenge traditionally addressed through rule-based systems operating on fully parametric Building Information Models (BIM). However, in practice, building information is still predominantly conveyed through two-dimensional (2D) drawings, limiting the applicability of existing approaches. While recent advances in Computer Vision (CV) have enabled reliable detection and segmentation of elements in design drawings, these techniques are commonly treated as standalone recognition tasks. We propose a lightweight, computational framework for ACC directly from 2D drawings using CV as an enabling technology. We evaluate the applicability of CV workflows to support ACC through a case study, focusing on regulatory requirements for bathrooms. The paper describes the full computational pipeline, from drawing preprocessing and element segmentation to machine-interpretable representations suitable for ACC and up to the compliance checking process. Results indicate that CV technology is a viable computational bridge between traditional drawing-based engineering practices and automated compliance methods.
This paper addresses the gap between architectural designers and computational design support developers by proposing a Lakatosian framework grounded in empirical design research. Combining Peirce's reasoning modes with Schön's reflective practitioner, we extend Pauwels et al.'s (2013) framework by revising the hypotheses on design thinking and tool adoption. We derive three guidelines: computational design support as aides of reasoning modes, as artefacts for experimenting, and as collaborative reasoning agents. For each guideline, we formulate a primary empirical research question. We also propose two empirical instruments to help answer those questions.
This paper presents a framework for information retrieval from the heterogeneous data sources in the construction sector. The core of the approach is a Large Language Model (LLM)-driven multi-agent system (MAS) consisting of specialized sub-agents and tools, each assigned to a specific data type. A host agent orchestrates the overall process. As a proof of concept, an implementation for Building Information Modeling (BIM) models and technical standards is presented. Challenges arise at the interfaces between LLM agents and the retrieval mechanisms. These are addressed by employing LangChain as an agent framework and the usage of the Model Context Protocol (MCP) as a novel standardized interface. The results demonstrate that such a MAS can answer complex queries that require information from multiple heterogeneous sources by combining several retrieval methods. The study provides a foundation for developing increasingly comprehensive systems through the application of various adaptation and optimization mechanisms.
