Requests for Information (RFIs) remain a critical yet poorly integrated component of construction workflows, typically managed as unstructured text disconnected from Building Information Modeling (BIM) data. Existing studies primarily focus on reducing RFI frequency or response time, with limited focus on linking RFI content to specific BIM elements. This study presents a data-driven framework that combines large language model (LLM)-based intent extraction with BIM metadata reasoning through a staged matching pipeline. By separating semantic cue extraction from rule-based element selection, the framework enhances interpretability and robustness. Validation on experimental and real-world datasets demonstrates reliable element and family-level retrieval.
Manual inspection remains subjective and poorly integrated with digital workflows. Although computer vision enables automated damage detection, most approaches fail to translate observations into parametric Building Information Modelling (BIM). For bridges, this limitation persists within Bridge Information Modelling (BrIM), where geometry, semantics, and lifecycle information must align. This paper addresses the gap through a four-stage framework: optimal reality modelling, hybrid damage perception, automated Scan-to-BIM, and semantic enrichment via IFC objects. Validated on operational railway bridges, the method achieves a damage-detection mAP50 of 0.621 and geometric deviations under 23 mm, enabling BrIM generation directly from visual data.
This paper introduces Adaptive Life Cycle Assessment (ALCA) as a time-resolved framework for continuously evaluating building environmental impacts over the lifecycle. Using a conceptual design science approach, the study develops a multidimensional framework integrating prospective and retrospective data and situates it within the Digital Building Logbook (DBL). An illustrative additive manufacturing case demonstrates data flows and feasibility. The results show that ALCA enables continuous updating of accumulated impacts, improves traceability, and supports PDCA-based environmental control. The framework addresses limitations of static and dynamic LCA and provides a governance-oriented approach for lifecycle impact monitoring and regulatory alignment.
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.
Data-driven approaches increasingly inform urban policy, yet the integration of intangible cultural heritage remains limited due to object-centric documentation and the separation of tangible and intangible domains. Existing frameworks conceptualize data as static records, inadequately supporting the semantic articulation of cultural meaning within urban contexts. This paper applies a semantic narrative approach based on a triadic model linking physical elements, procedural knowledge, and social practices. The framework structures heterogeneous cultural data into interpretable relational sequences. Through ecclesiastical heritage in Karachi, Pakistan, the study shows improved coherence between cultural data and policy-oriented interpretation and contributes to knowledge applicable to urban transformation.
Time-Series Foundation Models, pretrained on large amounts of time-series data, offer strong zero-shot forecasting capabilities. Recent TSFM versions provide improved support for covariate forecasting. This renders them a promising alternative to address current limitations of data-driven building performance prediction approaches: they are highly dependent on data availability, are mostly case-specific, and often favor correlation over causation. In this paper, we compare the predictive performance of two TSFMs with graph neural network approaches and a statistical baseline. We also present an approach for configuring causal covariates for TSFM predictions from graph representations. All models are evaluated using two real-world building datasets.
Air-handling units (AHUs) are major energy consumers in HVAC systems due to their coupled actuators and constrained multi-input multi-output operation. This paper proposes a data-driven Koopman–MPC framework that identifies lifted linear dynamics from building management system (BMS) data using EDMDc, enabling constrained MPC design for nonlinear AHU behavior. The approach is validated on a residential AHU in Stockholm, Sweden. Results demonstrate the feasibility of deploying Koopman-based predictive control using operational data, and a BMS-integrated edge implementation workflow is presented for practical building applications.
Digital Product Passports (DPPs) are emerging as mandatory, standardised sources of product information under EU policy. Yet their implications for construction-phase Lean workflows remain underexplored. Current practices still suffer from late discovery of product-related constraints, missing documentation, and weak traceability, undermining task readiness and plan reliability. This study combines a scoping search with conceptual mapping of DPP data categories to digital Last Planner System (LPS) decision points. Results include a DPP-to-LPS integration framework and construction-oriented guidelines for make-ready, commitments, and receiving/QA. The contribution is a practical research agenda for operationalising DPPs in Lean execution and enabling data continuity (e.g., DBLs-related).
Digital twin adoption is hindered by fragmented symbolic and subsymbolic construction data from heterogeneous sources. We propose a hybrid knowledge‑graph and vector‑database architecture that integrates a GeoSPARQL‑enabled RDF store with semantic embeddings to unify technical documentation and support both precise reasoning and fuzzy retrieval. A hybrid RAG workflow on PDF‑based data demonstrates improved ranking quality and contextual relevance compared to single‑paradigm retrieval. This lays the groundwork for future work on infrastructure-focused digital twins. Comprehensive support for multimodal data and case studies, such as minimally invasive bridge strengthening and scaffold‑free robotic assembly, is left as a planned continuation of the research.
