A database tool to support building envelope failure diagnosis
Bibliographic record
Abstract
This thesis' objective is to develop a tool to support the diagnostic process during a building envelope investigation. Access to the files of the Quebec Home Builder's Association (APCHQ) allowed information on residential building envelope failures in Quebec to be collected and organized into a computer database. Analysis of the database information pointed to water infiltration as the most frequent failure in the first 5 years of life of the houses. Inappropriate design accounted for 43% of the problems, 25% were due to deficient workmanship, and 11% to wrong detailing. The database built in this research could be a framework to collect and disseminate information about problems in the building envelope in Quebec and Canada. Thus, with it, a portrait of the current building envelope problems can be made. The prototype tool was designed and implemented to demonstrate how a tool integrating a database populated with well-documented and relevant cases of failure can be used in in-situ investigations. The tool works by supplying relevant similar cases of envelope failures. Developed using Microsoft Access, it allows delving into the 96 cases of the database and automates the production of reports. An evaluation of the prototype tool finally led to the development of guidelines for the next generation of a diagnostic support tool. Such tool should combine retrieving of similar cases with interrogation of relevant general knowledge in order to make a diagnosis and propose remedial measures. (Abstract shortened by UMI.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".