Bibliographic record
Abstract
The inherent strength of collapsible soils in their desiccated state is often compromised upon exposure to moisture.This poses a formidable challenge for foundation engineers when designing under such conditions.The intricacies of foundation design are exacerbated by the variability in treatment protocols, contingent on factors such as collapsible soil depth and the structural demands of the intended edifice.Deep foundations, often indispensable, facilitate the transfer of loads to more resilient strata beneath the collapsible soil matrix.This study pioneers the conception of an innovative foundation system calibrated to navigate the complexities of collapsible soils.Serving as an avant-garde foundation support mechanism, the novel system amalgamates raft foundations, steel cylindrical piles, and encapsulated and stabilized stone columns within an integrated framework.Employing sophisticated numerical analysis via Plaxis 3D, the performance of this hybrid system within collapsible soils is rigorously evaluated.Findings underscore the transformative potential of coupling reinforced stone columns with piles, culminating in a substantial augmentation of load-bearing capacity.Optimal performance is manifested when reinforced stone columns are judiciously positioned at the core of the raft foundation, complemented by peripheral pile deployment.Beyond load-bearing enhancement, the novel foundation system exhibits an unparalleled propensity for elevating ground quality and optimizing soil foundation performance.This innovation transcends the efficacy of traditional pile solutions and supersedes stone columns in terms of ground improvement.Moreover, the amalgamated foundation system is postulated to catalyze a transformative alteration in soil foundation dynamics, engendering a composite ground of elevated capabilities.The study further introduces a pioneering analytical model poised to prognosticate the carrying capacity of this integrated foundation system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".