Towards Smart Building Maintenance: Leveraging Multi-Source Data and Digital Twins with Ecological Momentary Assessment
Bibliographic record
Abstract
Complaint management, fault detection and diagnosis (FDD) are critical for building performance and occupants' well-being.Facility management addresses these services with work order logs containing occupant complaints and FDD identifying system faults.This thesis enhances these aspects using existing and new data sources.The first section maximizes existing data (Chapters 2 and 3).Text analytics (Chapter 2) is used to create benchmarks for maintenance performance evaluation and transforms data into visual insights.Additionally, facility management-enabled building information models (FM-BIMs) (Chapter 3) integrate maintenance logs with BIM, enhancing decisionmaking through contextual data exchange.A case study is presented to demonstrate the effectiveness of this methodology.The second section employs novel data collection methods and their application in digital twins (Chapters 4 and 5).Cozie smartwatch app representing micro-ecological assessment gathers occupants' thermal comfort and air quality feedback.This data source is integrated with building system sensor data.FDD rulesets utilize this data for fault detection, validated by occupants' accurate feedback.Surveys revealed that occupants seldom report discomfort unless the temperature deviates by 3°C.This approach reduces reporting thresholds, enhancing issue reporting by 29%.Chapter 5 introduces a digital twin framework for maintenance, integrating diverse building system data and real-time insights.Occupant feedback collection methods are diversified through online surveys and a smartwatch app, enhancing indoor quality assessment.The framework supports two tiers of analysis and user interfaces for facility management, demonstrated via a prototype that leverages existing infrastructure.This iii methodology allows facility management agents to develop digital-twin-enabled workflows bringing multiple real-time data streams together to make more informed decisions.In conclusion, this thesis advances complaint management, FDD, and other maintenance practices by innovating existing and new data sources.The proposed methodologies, from text analytics to digital twin frameworks, empower facility management to enhance building performance and occupant satisfaction proactively.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".