Application of statistical models in proportioning lightweight self-consolidating concrete with expanded clay aggregates
Bibliographic record
Abstract
A response surface method based experimental study was carried out to model the influence of key parameters on the properties of Lightweight Self-Consolidating Concrete (LWSCC) mixtures developed with expanded clay (EC) aggregates. Three key mix design parameters were selected to derive mathematical models for evaluating fresh and hardened properties. Water to binder ratio of 0.30–0.40, high range water reducing admixture (HRWRA) of 0.3–1.2% (by total content of binder) and total binder content of 410–550 kg/m 3 were used for the design of and testing of twenty LWSCC mixtures. Slump flow diameter, V-funnel flow time, J-ring flow diameter, J-ring height difference, L-box ratio, filling capacity, sieve segregation, fresh/28-day air/oven dry unit weights and 7- and 28-day compressive strengths were evaluated to analyze influence of mix design parameters and develop the models. Utilizing the developed models, three optimum expanded clay LWSCC (EC-LWSCC) mixtures with high statistical desirability were formulated and tested. It was possible to produce robust EC-LWSCC mixtures that satisfy the European EFNARC criteria for Self-Consolidating Concrete (SCC). The proposed mix design models are proved to be useful tools for understanding the interactions among mixture parameters that affect important characteristics of EC-LWSCCs. This understanding might simplify the mix design process and the required testing, as the model identifies the relative significance of each parameter, provides important information required to optimize mix design and consequently minimizes the effort needed to optimize LWSCC mixtures, and ensures balance among parameters affecting fresh and hardened properties. Examples highlighting the usefulness of the models are presented using isoresponse surfaces to demonstrate single and coupled effects of mixture parameters on measured properties. LWSCCs with EC lightweight aggregates can reduce the construction pollution, increase the design solutions, extend the service life of the structure and hence, promote sustainability in construction industry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".