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A review of the world experience in full-scale testing of existing reinforced concrete structures

2024· review· en· W4403247456 on OpenAlexaboutno aff
Oleg Kabantsev, Andrey Lapshinov

Bibliographic record

VenueVestnik MGSU · 2024
Typereview
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsScale (ratio)Reinforced concreteForensic engineeringStructural engineeringEngineeringGeographyCartography

Abstract

fetched live from OpenAlex

I ntroduction. Domestic and foreign experience of full-scale tests is analyzed. The retrospective of development of standards for conducting full-scale tests in Russia and the USSR is presented. The review of the world regulatory documents regarding the requirements for conducting full-scale tests of structures is carried out, which showed insufficient research of this question both in our country and abroad. The obtained review can be the basis for the development of the national standard of the Russian Federation for conducting full-scale tests. Materials and methods. Analytical review. Results. The state of the issue and the application of stress tests (full-scale tests) in our country, as well as in Germany, the USA, Italy, Canada, Australia, Switzerland and other countries are shown. The minimum requirements for the target test load during testing, the loading mode and acceptance criteria for the experimental assessment of the load-bearing capacity of existing load-bearing structures specified in various standards are given. Specialized criteria for the acceptance of load-bearing structures, such as the permanency ratio and deviation from linearity index, mentioned in individual standards, are analyzed. Conclusions. The similarity of common approaches and the lack of unified requirements for conducting this type of research and evaluating their results are noted. The necessity to develop a national standard of the Russian Federation for full-scale tests of erected reinforced concrete structures is considered.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.367
Teacher spread0.264 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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