Evaluation of foundation settlement characteristics and analytical model development
Bibliographic record
Abstract
Foundation settlement characteristics were evaluated based on standard penetration \ntest (SPT) results obtained from the six zones of Nigeria using some conventional \nanalytical models and numerical modelling. The study aimed at developing an \nimproved approximation of foundation settlement based on numerical modelling \nmethod that better represents soil constitutive behaviour and to determine the most \nappropriate settlement prediction analytical methods that are most suitable to Nigerian \nsoil peculiarities based on SPT data, being the most commonly used geotechnical field \ntest in Nigeria. Footing dimension of size and applied foundation \npressure of 300 kN/m2 \nat foundation embedment depths of 0.6, 2.1, 3.6, 5.1, 6.6, 8.1, \n9.6, 11.1 and 12.6 m were considered. Results show that the predicted compressibility \nis higher in the southern zones compared with their northern counterparts based on the \nrecommendation of Eurocode 7 which allows a maximum total settlement of 25 mm \nfor serviceability limit state. Based on the numerical analysis results using Plaxis 3D \nFoundation, a finite element code package, it was observed that settlement prediction \nmethods proposed by Schmertmann et al., Burland and Burbidge, Canadian \nFoundation Engineering Manual and Mayne and Poulos gave good estimations of \nfoundation settlement among others. The analytical models developed with soil \nparameters N60, angle of internal friction and Poisson ratio as predictor gave the best \nresults and are recommended for foundation settlement prediction.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".