MétaCan
Menu
Back to cohort

Field Applications of Basalt Fiber Materials for Rehabilitation of Deteriorated Concrete Structures

2020· article· en· W3038939099 on OpenAlexaffabout
Jason Duic, John Branston, Adeyemi Adesina, Sreekanta Das, David Lawn

Bibliographic record

VenueJournal of Performance of Constructed Facilities · 2020
Typearticle
Languageen
FieldEngineering
TopicInnovative concrete reinforcement materials
Canadian institutionsWindsor Clinical ResearchUniversity of Windsor
Fundersnot available
KeywordsBasalt fiberCivil engineeringBasaltRehabilitationEngineeringConstruction engineeringForensic engineeringEnvironmental scienceFiberMaterials scienceGeologyComposite material

Abstract

fetched live from OpenAlex

Deterioration of concrete structures is imminent with time, especially those subjected to the harsh environment in places like North America. The high cost associated with the replacement of these concrete structures has called for the development of innovative ways in which these structures can be rehabilitated effectively in a sustainable way without a huge economic burden. Several ways to repair deteriorated concrete structures have been developed over the years; however, some of these repair methods are expensive and not effective. Also, several innovative methods and materials have been studied in the past years; however, these materials and methods are limited to laboratory applications. To foster a sustainable and economic way of rehabilitating concrete structures, various basalt fiber products that have been extensively evaluated in the laboratory at the University of Windsor were used for rehabilitation of field concrete structures. Two concrete bridges located in Ontario, Canada, were selected for the case studies. Different types of basalt fiber products—chopped fibers, fabric, mesh, and rebar—were used to rehabilitate these existing structures. The field study shows that the use of basalt products is a sustainable and economical method for the rehabilitation of deteriorated reinforced concrete 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Performance of Constructed FacilitiesSame topicInnovative concrete reinforcement materialsFrench-language works237,207