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Record W2417916530 · doi:10.1061/9780784479902.068

A Risk-Based Structural Assessment Approach for Port Metro Vancouver’s Asset Management

2016· article· en· W2417916530 on OpenAlexaffabout
Houman Ghalibafian, Laura Quiroz, Gary St. Michel, Mo Mofrad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicEngineering and Material Science Research
Canadian institutionsTetra Tech (Canada)Positive Living Society of British Columbia
Fundersnot available
KeywordsAsset managementReliability (semiconductor)Asset (computer security)Port (circuit theory)Life-cycle cost analysisReliability engineeringMeasure (data warehouse)Risk analysis (engineering)Risk managementRisk assessmentLife cycle costingComputer scienceEngineeringOperations managementBusiness

Abstract

fetched live from OpenAlex

This paper presents a risk-based structural assessment methodology used in conjunction with a life cycle cost analysis to assist Port Metro Vancouver develop a multi-year works program with funding needs for maintaining the reliability of its assets. The risk measure is based on the probability of structural failure and the monetary consequence of failure, and accounts for the deterioration of material over time. For steel, the corrosion rate was used as the measure of deterioration. For reinforced concrete, material modelling estimated the rate of degradation, the onset and the progression of rebar corrosion over time. The failure probabilities were estimated first by assuming that no repair or rehabilitation is performed. Then various scenarios of preventative risk mitigation measures were considered, and their associated costs were used in the life cycle cost analysis to develop strategies for asset management. An example of the completed projects, the merits, and the limitations of this approach are presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.309
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes2
Has abstractyes

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