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Record W2900114947 · doi:10.1115/ipc2018-78740

Flood Monitoring: Evaluating Action Response Time Relative to Warning Time

2018· article· en· W2900114947 on OpenAlexaff
S. L. Davidson, Gerald R. Ferris, Joel Van Hove, Joel Babcock, Jan Bracic

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2018
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsFlood mythWarning systemRisk analysis (engineering)Pipeline (software)Computer scienceFlooding (psychology)Pipeline transportEnvironmental scienceReliability engineeringEngineeringBusinessEnvironmental engineeringGeography

Abstract

fetched live from OpenAlex

Flood monitoring is one method currently being used by the pipeline industry to provide alerts when flooding is approaching, or has exceeded, levels that could create hydrotechnical conditions that threaten pipeline integrity. Flood monitoring does not provide protection from hydrotechnical hazards or reduce the probability of failure, but can lower risk by providing advanced warning, allowing operators to initiate actions that reduce the consequences of failure in the rare event that pipeline integrity is threatened by hydrotechnical forces. Pipeline pressure reduction, shut-in, purge, and spill response mobilization are all examples of actions commonly used to reduce failure consequence. However, these actions require time to execute, ranging from a number of hours to a number of days, depending on factors such as site location, valve spacing, and product type. The effectiveness of flood monitoring as a consequence reduction strategy is contingent on having sufficient time to implement the flood response action. In designing a flood monitoring program, it is necessary to ask: can flood monitoring provide sufficient advanced warning for an action plan to be fully executed before pipeline integrity is compromised? The present study evaluated 35 high priority pipeline watercourse crossings, to estimate the flood return periods at which actions could be taken that correspond to warning times of 12, 24, 48, and 72 hours before the critical flood (i.e., a conservative estimate of the flow at which fatigue failure is considered possible) and to evaluate the feasibility of flood monitoring as a short-term risk management strategy prior to mitigation. The 35 crossings are currently scheduled for mitigation and rely on flood monitoring as an interim risk management tool. The rate of increase in flood discharge during all previously recorded flood events at each real-time monitoring gauge was first obtained to estimate the rate of flow increase during the critical flood event. Of the 35 crossings, 33 had a maximum warning time of less than 48 hours. Using a 24-hour warning time, 10 of the 35 crossings have a warning flow of less than a 1 in 5-year flood. The results show that the ‘action initiation flood level’ for more than 90% of the most susceptible watercourse crossings may be too low to be practical; at crossings where more than 48 hours of response time is required, flood monitoring may not significantly reduce hazard consequence as the action response plan may not be fully executed prior to pipeline failure. Pipeline failures are rare, and flood monitoring provides a useful monitoring approach for short-term management in many watercourses. However, these results demonstrate the importance of evaluating the required action response time relative to the available warning time for each watercourse crossing to confirm that flood monitoring will achieve the risk reduction expected by the operator. If flood monitoring is determined to be impractical because the action initiation flood is too low, it may provide justification for initiating other management actions (e.g., flood forecasting, purging prior to the flood season, or elevating such sites on the priority list for physical repairs).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.268
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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