Exploring the Validity of Design Storms as Tools to Size and Design Stormwater Infrastructure for Urban Sewersheds
Bibliographic record
Abstract
Four conceptual stormwater systems have been designed using City of Toronto and City of Pickering standards. The four systems have been inputted into EPA SWMM 5.1 and simulated against IDF curve-based design storms, as well as Toronto’s historic rainfall data ranging from 2005 to 2015. From analyzing the simulation outputs, it is noted that many of the design assumptions lead to systems performing below expectations when tested directly against the design storm. In some cases, peak flow rates, flow velocities and flow areasall exceed the expected values from the initial design. There are several factors that may contribute to this. The first notable factor would be an incorrectly assumed time of concentration, leading to a design storm that is not representative of an actual “worst case scenario” storm. The second notable factor is the inclusion of all pipes in a dynamic network, as opposed to the simplistic Manning’s approach taken by the design. These two factors are explored and their potential impacts are discussed in relation to real world situations where storms are far less likely to mimic the chosen design storm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".