Envelope Remediation—A Case Study in Support of an Over-Cladding Approach
Bibliographic record
Abstract
Abstract This paper addresses a case study in which a contemporary, high-performance glass curtain wall and a composite metal panel cladding system were installed over an existing failed 1960s masonry wall with aluminum windows. In addition to consideration of the typical performance requirements for air and water infiltration and thermal insulation, the new wall systems were designed to meet glass shard retention requirements and to accommodate improvements to the structural frame required by the General Services Administration. These performance requirements were all met while creating a more inviting exterior appearance, improving the interior work environment, extending the building’s service life, and reducing energy consumption. Problems of the original design and construction, extent of previous remediation attempts, findings of the investigation of the existing building, and the development of remediation options are discussed to provide background to selection of the over-cladding approach for remediation. Opportunities and limitations posed by the existing building, LEED requirements, glass shard retention, construction strengthening, full and uninterrupted occupancy, as well as other challenges inherent in the over-cladding of an existing high-rise building on a tight urban site are identified and discussed, and positive results are identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".