Analyse et prévision des caractéristiques du pompage du béton auto-plaçant à haute résistance
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".