Digital Twin and BIM synergy for predictive maintenance in smart building engineering systems development
Bibliographic record
Abstract
The rapid evolution of smart building engineering has redefined how modern infrastructure is designed, operated, and maintained. At the intersection of this transformation lies the convergence of Digital Twin technology and Building Information Modelling (BIM), offering a dynamic and data-driven approach to predictive maintenance. Digital Twins, which serve as real-time virtual replicas of physical assets, when integrated with the information-rich environment of BIM, enable enhanced visibility, control, and foresight into building system performance. This synergy bridges the gap between design and operation, fostering a proactive maintenance culture within increasingly complex built environments. This paper investigates how the integration of BIM and Digital Twin frameworks supports predictive maintenance strategies in smart building systems. It explores the foundational principles of each technology and examines their interoperability in creating self-aware, responsive infrastructures. Emphasis is placed on real-time sensor integration, historical data mapping, anomaly detection, and the simulation of future scenarios to anticipate system failures before they occur. Through the implementation of Digital Twin-BIM ecosystems, facility managers and engineers gain continuous insights into HVAC, lighting, structural, and safety systems, thereby reducing downtime, optimizing performance, and extending asset life cycles. The study also outlines the challenges in deploying this hybrid model, including data standardization, interoperability gaps, and the need for cross-domain collaboration. Case references illustrate how early adopters have leveraged this synergy for smart facilities management and sustainable building lifecycle planning. Ultimately, the convergence of Digital Twins and BIM represents a paradigm shift toward intelligent, self-maintaining infrastructure, signaling a new era of digitally augmented engineering practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".