Water Distribution System Reliability Under a Fire Flow Condition: A Probabilistic Approach
Bibliographic record
Abstract
In the technical literature concerned with the design of water distribution systems (WDS), the term `fire flow' is often associated with three distinct challenges: to estimate the actual fire flow needed to fight a fire; to estimate the availability of fire flow in a specific WDS; or, to evaluate the potential impacts of fire flow on WDS reliability. The first two challenges are generally addressed from a deterministic perspective, and, although some probabilistic approaches exist for solving the latter one, they seldom address fire flows explicitly. In general, fire flows are considered as part of a unique nodal flow probability distribution for reliability analysis purposes. Yet one significant question remains consistently unanswered: what is the WDS reliability given that a fire situation occurs? The objective of this study is to present a methodology which can be used to evaluate the WDS reliability under a fire condition. To this end, two different probability distributions are assessed for the nodal flows: one representing the base demands and the other representing the fire flow demands. A Monte Carlo method is used to generate the flows, and steady-state hydraulic, simulations are performed to derive both nodal and system reliability indexes for a WDS under a fire condition.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".