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Record W3012227195 · doi:10.1139/cjce-2019-0580

Steel buried structures: condition of Ontario structures and review of deterioration mechanisms and rehabilitation approaches

2020· article· en· W3012227195 on OpenAlexaffvenueabout
Robert Cichocki, Ian D. Moore, Kevin Williams

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsAtlantic Industries (Canada)Queen's University
Fundersnot available
KeywordsRehabilitationBridge (graph theory)Driver rehabilitationEngineeringCulvertForensic engineeringLeverage (statistics)Civil engineeringConstruction engineeringStructural engineeringComputer sciencePsychology

Abstract

fetched live from OpenAlex

Buried steel structures, commonly referred to as buried bridges, culverts, or soil–steel structures are a valuable bridge crossing solution. Owners manage their bridge assets by evaluating their condition and rehabilitating as required. Ontario’s resources for managing and rehabilitating buried steel bridge structures are limited, and an investigation into the maintenance and rehabilitation practice of Ontario’s assets demonstrates a lag in their maintenance and rehabilitation. Knowledge regarding rehabilitation of these structures is dispersed and unconcise, leaving owners challenged to understanding how to best manage and rehabilitate their assets. This paper investigates the age, condition, and rehabilitation of steel buried bridges in Ontario and reviews the commonly encountered deterioration and distress mechanisms along with the state-of-the-art rehabilitation practices. With an understanding of structural behavior, deterioration, and rehabilitation opportunities for structures nearing the end of their service lives, owners will be better equipped to effectively manage their inventory and leverage the economic, social, and environmental value of buried structures.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.701

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 head0.011
GPT teacher head0.178
Teacher spread0.167 · 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

Citations10
Published2020
Admission routes3
Has abstractyes

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