An Optimized Prefabricated Raft Footing System for Houses on Shrink-Swell Soils: Preliminary Results
Bibliographic record
Abstract
The strong demand for houses has been hampered by a shortage of skilled labor in Australia, which can be potentially alleviated using prefabrication. Significant advancements in the design and construction of prefabricated houses have been observed; however, most substructure constructions still use traditional cast-in-place method that is labor intensive and weather-dependent. Prefabrication of footing systems is an advantageous solution since this require minimal manual labor and shorter construction period. The design of an innovative prefabricated footing needs to consider structural integrity and design assembly. One of the important structural issues for light-weight houses is cyclic differential ground movements affecting footing systems due to reactive soils. This shrink-swell movements are due to the decrease and increase in soil moisture, which can cause minor to severe damage depending on the presence of fines. Due to the issues on shortage of skilled labor and housing, and the costly impact of shrink-swell movements of reactive soils to footings, this study aims to develop a prefabricated footing based on optimized waffle raft. The developed system can easily be installed in stable to highly reactive sites, minimizing site disturbance, on-site assembly requirements and maximizing construction speed, quality and sustainability.
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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) | 0.001 | 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) | 0.003 | 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".