Performance Based Design: Why Aren’t We Leveraging It to Its Full Potential? Managing Opportunity, Safety, and Contract Risk
Bibliographic record
Abstract
The City of Toronto is in the middle of “The Big Move”, a 25 year, $50B plan to address congestion in the Greater Toronto and Hamilton Area (GTHA). This includes 7 new subway or light rail transit lines in various stages of planning or construction, and corresponding major urban densification along major rail corridors in the city. This paper will show how transit infrastructure projects, and projects adjacent to major rail corridors in the GTA, are less economic due to prescriptive based design requirements compared to similar complexity local private projects. Using case histories from several recent projects, the paper will compare adjacent specification driven design and performance-based design examples to show the potential when leveraging performance-based design methodologies safely and effectively rather than a “paint by numbers” approach to design. The paper will describe the risk specification driven design parameters can have on design economy, constructability, and schedule, and outline proposed tendering methodologies for incorporating performance-based designs into contracts to limit risks to both Owners and Contractors.
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 | 0.036 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".