Investigating the fresh and mechanical properties of wood sawdust-modified lightweight geopolymer concrete
Bibliographic record
Abstract
Geopolymer concrete has developed as a potential alternative to ordinary Portland cement-based concrete, wherein various industrial by-products have been converted as beneficial spin-offs. Apart from appropriate compressive strength in the construction sector worldwide, the durability, sound absorption, thermal conductivity, and weight of concrete are also major concerns. Lightweight geopolymer concretes have gained attention because of their superior strength, durability, lower environmental impact, and sustainable characteristics. In this view, the current study examined the feasibility of using sawdust as a natural fine and coarse aggregate substitution in fly ash (FA)-granulated blast furnace slag (GBFS) based geopolymer concrete. Four mixes with a different percentage of sawdust (25, 50, 75, and 100) substituting natural aggregate were designed to examine the effects of sawdust on fresh and hardened features of geopolymer concrete compared to those conventional FA-GBFS-based geopolymer concrete with natural aggregate. Sodium silicate (NS) and sodium hydroxide (NH) (with NS/NH ratio of 0.75) were utilized to dissolve the alumina silicate from FA and GBFS. Informational models were developed using an experimental dataset to estimate the compressive strength of geopolymer concrete mix designs. Besides, using the weight of the developed network, a global sensitivity (GS) analysis was developed to identify the sensitivity of compressive strength to the waste sawdust content. Test results confirmed that by substituting natural aggregate with 100% sawdust, there was around a 35% decrease in compressive strength. Nevertheless, the sound absorption coefficient was increased by an average of 38% in frequencies range between 1800 and 2500 HZ, and thermal conductivity decreased by around 4.5 times once the natural aggregate was substituted by 100% sawdust.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".