Characteristics of Lightweight Cellular Concrete and Effects on Mechanical Properties
Bibliographic record
Abstract
This study investigated the pore structure and its effects on mechanical properties of lightweight cellular concrete (LCC) in order to understand more and detailed characteristics of such structure. As part of investigation, environment scanning electron microscopes (ESEM) and industrial high-definition (HD) macro photography camera were separately used to capture and compare images of specimens. Physical properties of the pore structure, including pore area, size, perimeter, fit ellipse, and shape descriptors, were studied based on the image processing technology and software applications. Specimens with three different densities (400, 475, and 600 kg/m3) were prepared in the laboratory. Firstly, the effects of density on the characteristics of pore structure were investigated; furthermore, mechanical properties (compressive strength, modulus of elasticity and Poisson’s ratio, flexural strength and splitting tensile strength of LCC) were tested. The relationships among pore characteristics, density, and mechanical properties were analyzed. Based on the results obtained from the lab test—comparisons made between specimens with high-densities and those with low-densities—it was found significant variability in bubble size, thickness, and irregularity of pores. Furthermore, the increase of density is accompanied by better mechanical properties, and the main influencing factors are the thickness of the solid part and the shape of the bubble. The thicker of solid part and more regular pores of LCC has, the better mechanical properties are.
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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 | 0.001 |
| 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".