Operational performance benchmarking for commercial buildings by using text analytics on work order logs and tenant survey data
Bibliographic record
Abstract
Operational performance metrics are necessary to detect anomalous floors, equipment, and work-order categories in large commercial buildings.To this end, this thesis presents a method to extract operational performance metrics from computerized maintenance management systems (CMMS) and text-based tenant surveys.The method was demonstrated by using work-order logs and text-based tenant survey data gathered from four large commercial buildings in Ottawa, Canada.The analysis of CMMS data highlights the potential of decision trees, Sankey diagrams and association node networks to effectively visualize anomalies in building complaint patterns.Investigation of the text-based tenant surveys using established text analytics algorithms reveals that classifiers are more accurate for sentiment analysis than lexicon-based methods while both association rule mining and topic modelling algorithms successfully uncover key operational insights.Finally, a software tool mock-up was developed that combines the most impactful elements from previous work for building owners to visualize complaint patterns and maintenance workflows.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".