Development of a Strength Prediction Model for “Green” Compressed Stabilised Earthbricks
Bibliographic record
Abstract
Traditional fired clay bricks are widely used as a fundamental building material in most countries. Availability, low costs and low-skilled labour are main factors that have made the bricks a popular choice. However with rising awareness to reduce carbon footprint and promote sustainable development, earth-making has taken a different path to minimize the environmental impact. Compressed stabilised earthbrick is an example of the alternative building material. Various efforts have been directed to develop these bricks, including the use of different binding agents, raw materials and technology. In conjunction with these progresses, and considering that strength is the primary concern in brick-making, it was conceived that a strength prediction model ought to be established to assist in the bricks’ production, especially in the mix design stage. In collaboration with a local property developer, the Research Centre for Soft Soils (RECESS) has embarked on an industrial research project to develop “green” sustainable compressed stabilised earthbricks in situ for a large scale mixed development site. As part of the joint research effort, a series of trial specimens were prepared at different mix ratios using soil samples retrieved from the site. The specimens were next examined with the conventional compressive strength test, coupled with the novel non-destructive S-wave velocity measurement. The test results were then analysed and cross-correlated to establish a strength prediction model for the bricks produced. The charts relating the relevant parameters serve not only as a quick guide to the expected strength, but also provide insights to the behaviour of compressed stabilised material under loading.
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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 | 0.001 | 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".