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Record W2252289531

Analyse et prévision des caractéristiques du pompage du béton auto-plaçant à haute résistance

2013· article· fr· W2252289531 on OpenAlexaboutno aff
Rami Khatib

Bibliographic record

VenueKnowledge UdeS (Institutional Deposit of the University of Sherbrooke) · 2013
Typearticle
Languagefr
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTonHumanitiesArtGeographyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

Modern construction practices require proper knowledge to predict concrete pumping pressure, especially in high-volume and high-rise applications. Despite the progress made over the last decades, the spread of concrete pumping to high-rise construction has been hampered by the lack of standardized operating procedures and performance criteria. By and large, the guidelines available today focus predominantly on pumping Conventional Vibrated Concrete (CVC), while ambiguity still surrounds pumping Self-Consolidating Concrete (SCC) and other types of Highly-Workable Concrete (HWC). This PhD dissertation focuses on the fundamental principles relevant to the flow of high-strength SCC in pumping pipes, and it aims to develop methods to predict and reduce the required pumping pressure. The flow pattern of SCC in pipes is analytically investigated, providing a numerical approach to predict the pumping pressure based on the properties of both concrete and the lubrication layer, the pipe diameter, and the flow rate. The analytical results are further validated through full-scale pumping tests executed at the laboratory of the Université de Sherbrooke. Through this phase 26 optimal concrete mixtures were pumped in a 30-m pumping circuit to investigate the interactions between the concrete properties and pressure loss. The same tests are also employed to empirically correlate pressure loss with rheological and tribological properties of concrete at different flow rates. The resulting correlations furnish instrumental models capable of computing pressure loss for a wide range of concrete properties. In another application, the experimental results are analyzed to identify the influence of pumping on concrete properties with time. Full-scale pumping results are statistically analyzed in order to establish a quantitative description of the most influential parameters governing the concrete flow in pipes. As a result, concrete pipe flow is statically modeled, allowing the computation of pressure loss at different flow rates based on the the rheological and tribological properties of the concrete and the pipe diameter. Another statistical model is derived to calculate the pressure loss as a function of the V-funnel flow time, granting the advantage of predicting the pressure loss on job sites without the need for complex rheological and tribological measurements. In light of the research findings of the previous phases, a new simple test method called the pipe flow test (PFT) is developed in the context of this research, specifically for predicting pipe flow pressure loss. With preceding research phases as insights, the final stage of this project is directed toward mix design optimization faced with the challenge of reducing the pumping pressure and meeting the strength requirements of high-strength SCC. Ultimately, the research findings emanating from this investigation provide practical guidelines and conclusive models to predict and reduce pumping pressure for a wide scope of concrete mixtures and pipe diameters.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.008
GPT teacher head0.194
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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