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Record W1998163104 · doi:10.1115/ipc2014-33474

Effective Consequence Evaluation for System Wide Risk Assessment of Natural Gas Pipelines

2014· article· en· W1998163104 on OpenAlexaff
Aleksandar Tomić, Shahani Kariyawasam, Pauline Kwong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsRisk assessmentRisk analysis (engineering)Risk managementPipeline (software)PopulationComputer sciencePipeline transportRanking (information retrieval)Probabilistic logicReliability engineeringEngineeringArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

System Wide Risk Assessment (SWRA) is an integral part of an Integrity Management Program (IMP), and it is the first step in most IMPs. Risk is the expected value of loss (often expressed as damage per year, i.e. expected number of annual injuries or fatalities). Risk is calculated as the product of the Probability/Likelihood of Failure (LoF) and the consequence of failure, where failure is defined as a loss of containment event. Hence, it is necessary to calculate both the Likelihood and the consequences of failure in order to accurately model risk. For natural gas pipelines, consequence is primarily human safety-based. The primary threat to the population is the effect of the thermal radiation due to ignited pipeline ruptures. Currently, most pipeline industry system wide risk assessment models are qualitative risk models, where consequence is generally characterized by class, relative population measures, or some other relative measure. While this may be adequate for some relative risk ranking purposes, it is generally not accurate in representing the true consequences and the arbitrary nature leads to poor representation of actual consequences. Qualitative risk models are also highly subjective, and can have a high degree of bias. Thus, in this study, quantitative LoF assessment and a rigorous quantitative consequence model were used to make the risk assessment process more accurate, more objective, and transparent. The likelihood algorithm developed in this study is described in a companion paper. It should be noted that a quantitative estimate is never completely objective as subjective assumptions and idealizations are still involved, however it provides a framework to make it as objective as possible. The consequence model implemented in this study is highly quantitative, and it depends on the pipeline properties (i.e. diameter, MAOP etc.) in addition to the structure properties (i.e. precise location and type of structures). The lethality zone utilized in the consequence model is a curve which has 100% lethality at the point of rupture but recedes in lethality as the point of concern moves away from the rupture location. The lethality curve is calculated using the PIPESAFE software [6] that is developed by rigorous analytical, experimental, and verification work. This ensures that the lethality curves are pipeline specific. Furthermore, the position of the structures inside the lethality zones is taken into consideration, which means the structures located closer to the pipeline see a higher degree of lethality than the structures further away from the pipeline. Risk is represented by two specific, well defined measures: Individual Risk (IR), and Societal Risk (SR). These two measures are well accepted concepts of risk that go beyond the pipeline industry, and are particularly used in the pipeline industry in countries where quantitative risk is required by regulation (e.g. UK and Nederlands). IR takes into account the inherent risk of the pipeline to the single individual who may happen to be in the vicinity of the pipeline. SR, on the other hand, takes into account known population centers, settlements, and structures to define the risk to communities. When risk is calculated quantitatively, it is possible to use well defined and widely accepted criteria to determine the acceptability of risk in terms of IR and SR criteria for all pipelines. The advantages of using IR and SR are discussed and shown through implemented examples.

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)0.014
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.890
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.429
Teacher spread0.380 · 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 designSimulation or modeling
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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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