Nonlinear Settlement of Piled Rafts in Sandy Soil
Bibliographic record
Abstract
A hand-calculation method for estimation of nonlinear load–settlement response of piled raft foundations (PRFs) embedded in sandy soils is developed using three-dimensional finite-element (FE) analysis in which the elastoplastic constitutive behavior of the soil is modeled using the unified Clay And Sand Model (CASM). Three types of PRFs with rectangular, strip, and circular rafts and with a variety of pile dimensions and arrangements are considered in the study. The PRFs are assumed to be embedded in five different sands (Ottawa sand, Erksak sand, Sacramento sand, Portaway sand, and Decomposed Granite sand) with different elastic properties, critical state parameters, and relative densities. Systematic parametric studies are performed to develop equations for the estimation of the average nonlinear settlement of PRFs. The maximum and differential settlements are also estimated from the average settlement. The proposed equations require the relative density, elastic constants, and critical state friction angle of sand, and the piled raft geometry and properties as inputs. The settlement equations are applicable to PRFs of sizes similar to those considered in the study and can be used by practitioners for quick, initial estimation of PRF settlement as part of design calculations.
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 | 0.001 |
| 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".