Upgrading the capacity of foundations by using a hybrid expanded footing–micropile system
Bibliographic record
Abstract
This paper introduces an innovative approach, where a hybrid expanded footing–micropile system is utilised to improve the load-bearing capacity of shallow footings on loose sand. Finite-element analyses were carried out with a validated numerical model to simulate a shallow foundation on loose sand supporting a concentric vertical load. The simulated foundation was then upgraded by expanding its area and underpinning the additional area with pressure-grouted micropiles. The micropile installation process was treated as a pressure-controlled cavity expansion problem in the numerical simulation to account for the associated increase in radial stresses in the adjacent soil. A comprehensive parametric study was conducted, focusing on the primary determining factors: number of micropiles, micropile diameter, micropile bond length, grouting pressure, width of the additional area around the perimeter of the footing and footing aspect ratio. The results showed that the micropiles acted as excellent settlement reducers once installed. If reducing the settlement is a high priority, when designing an expanded footing, the hybrid expanded footing–micropile system should be preferred rather than expanding the footing without the use of micropiles. The load capacity of the underpinned foundations was highly dependent on the grouting pressure applied during micropile construction.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".