Lightweight Self-consolidating Concrete: Statistical Modelling, Mixture Design And Performance Evaluation
Bibliographic record
Abstract
A response surface method based experimental study was carried out to model the influence of key parameters on properties of Lightweight Self-Consolidating Concrete (LWSCC) mixtures developed with various types of lightweight aggregates namely, furnace slag (FS), expanded clay (EC), and expanded shale (ESH). Three key parameters were selected to derive mathematical models for evaluating fresh and hardened properties. Water/binder ratio of 0.30 to 0.40, high range water reducing agent (HRWRA) of 0.3 to 1.2% (by total content of binder) and total binder content of 410 to 550 kg/m3 were used for the design of LWSCC mixtures. Slump flow diameter, V-funnel flow time, J-ring flow diameter, J-ring height difference, L-box ratio, filling capacity, bleeding, fresh air content, initial and final set times, sieve segregation, fresh/28-day air/oven dry unit weights and 7- and 28-day compressive strengths were evaluated. Utilizing the developed model, three optimum LWSCC mixes with high desirability were formulated and tested for mechanical, mass transport and durability characteristics. The optimized industrial LWSCC mixtures were produced in lab/industrial set-up with furnace slag, expanded clay, and expanded shale aggregates. The mixtures were evaluated by conducting compressive/flexural/split tensile strength, bond strength (pre/post corrosion), drying shrinkage, sorptivity, absorption, porosity, rapid chloride-ion permeability, hardened air void (%), spacing factor, corrosion resistance, resistance to elevated temperature, salt scaling, freeze-thaw iv resistance, and sulphuric acid resistance tests. It was possible to produce robust LWSCC mixtures that satisfy the European EFNARC criteria for Self-Consolidating Concrete (SCC). The proposed mix design model is proved to be a useful tool for understanding the interactions among mixture parameters that affect important characteristics of LWSCC. 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. LWSCCs with FS, EC and ESH 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".