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Record W3212924934 · doi:10.32920/ryerson.14657295.v1

Bridge rehabilitation detailing manual (BRDM) : (Commentary on MTO structure rehabilitation manual)

2021· preprint· en· W3212924934 on OpenAlexaboutno aff
Behnam Mehraie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationBridge (graph theory)Christian ministryEngineeringConstruction engineeringEngineering managementOperations managementTransport engineeringMedicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

The Ministry of Transportation of Ontario (MTO) has developed few manuals that deal with inspection and rehabilitation of bridges; such as OSIM (Ontario Structural Inspection Manual), SRM (Structure Rehabilitation Manual), OPSS (Ontario Provincial Standards Specifications) and OPSD (Ontario Provincial Standards Drawings). Most of these manuals except OPSD do not provide detailed sketches to clarify the theory; therefore, practicing engineers in rehabilitation industry have difficulties during design and construction of rehabilitation projects. The main objective of this project is to provide a manual called "Bridge Rehabilitation Detailing Manual (BRDM)" for clarifying practicing engineers' problems. This manual presents the results of a wide research and studies on rehabilitation of about 80 bridges in Ontario. Most of these bridges are located at major locations of Ontario with high volume of traffic going over. Out of the 80 bridges, 62 bridges have been considered as case-studies since they were designed and detailed by top ranked and well-known consulting firms in rehabilitation industry. The most common deterioration and relevant rehabilitation methods were categorized in a database to help the development of the BRDM. This has been combined with MTO's manuals to result and act as commentary of MTO Rehabilitation Manual.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.248
Teacher spread0.242 · 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.

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
Published2021
Admission routes1
Has abstractyes

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