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Record W2082954011 · doi:10.1115/ipc2002-27242

A Hazard and Risk Management System for Large Rock Slope Hazards Affecting Pipelines in Mountainous Terrain

2002· article· en· W2082954011 on OpenAlexaff
Michael J. Porter, Alex Baumgard, K. Wayne Savigny

Bibliographic record

Venue4th International Pipeline Conference, Parts A and B · 2002
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsHazardTerrainNatural hazardPipeline transportHazard analysisMining engineeringPipeline (software)TraverseEnvironmental scienceEngineeringGeologyReliability engineeringGeographyCartographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Pipelines and other linear facilities that traverse mountainous terrain may be subject to rock fall and rock slide hazards. A system is required to determine which sites pose the greatest hazard to the facility. Once sites are ranked according to hazard exposure, a risk management program involving inspection, monitoring, contingency planning and/or mitigation can be implemented in a systematic and defensible manner. A hazard rating methodology was developed to identify and characterize rock slope hazards above a South American Concentrate Pipeline, and to provide a relative ranking of hazard exposure for the pipeline, an access road and operational personnel. The rating methodology incorporates the geometry of the right-of-way, estimated pipe depth, staff and vehicle occupancy time, failure mechanism and magnitude, and the annual probability of hazard occurrence. This information is used in a risk-based framework to assign relative hazard ratings within rock slope sections of relatively uniform hazard exposure. This paper outlines a general framework for natural hazard and risk management along linear facilities, describes the rock slope hazard rating methodology, and illustrates how the system was applied along a South American Concentrate Pipeline.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.601
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same venue4th International Pipeline Conference, Parts A and BSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207