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Highway Bridge Infrastructure in the Province of British Columbia (BC), Canada

2017· article· en· W2612659281 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInfrastructures · 2017
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBridge (graph theory)Transport engineeringRetrofittingStructural health monitoringPierCivil engineeringResilience (materials science)Vulnerability (computing)Christian ministryEngineeringForensic engineeringComputer scienceStructural engineering

Abstract

fetched live from OpenAlex

Some recent catastrophic impacts on highway bridges around the world have raised concerns for assessing the vulnerability of existing highway bridges in Canada. Rapid aging of bridge infrastructure coupled with increased traffic volume has made it crucial to establish an advanced Bridge Management System (BMS) for highway bridges. This paper aims at developing a highway bridge inventory for the province of British Columbia (BC) which is critical for efficient assessment of the existing structural health condition of the bridges, predicting their future deterioration, and prioritizing their maintenance and retrofitting works. This inventory is an extensive assemblage of data on highway bridges in BC under the responsibility of the BC Ministry of Transportation and Infrastructure (BC MoT) that includes more than 2500 highway bridges. It includes identification of the most common bridge types along with their location, structural and geometric parameters such as construction materials, bridge length, number of spans, deck width, skew angle, bridge pier, and foundation type, structural health condition rating and construction period. This information is of paramount importance for effective infrastructure management, proper rehabilitation solutions, and efficient design of a Structural Health Monitoring (SHM) and Control System for enhancing structural resilience of highway bridges in BC. Several statistical analyses have been carried out for efficient utilization of the information available in the inventory for further research and analyses, as well as for developing a proper BMS for the province’s bridges.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.543

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 science0.0010.000
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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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