Full-scale pile tests in sand and development of a computer program for predicting load capacity
Bibliographic record
Abstract
An interactive computer program GLAMPILE has been developed for predicting the static load capacity of single piles formed in any soil profile. A variety of well-known prediction methods have been incorporated into the program, including (i) soil mechanics based formulae; (ii) direct and indirect cone penetration test (CPT) based methods with and without accounting for scale effects of the cone on pile base capacity; and (iii) a new CPT-based method that considers the effects of critical depth and shaft resistance distribution, although the method has only been calibrated for relatively short piles. GLAMPILE can cope with different pile types installed with or without a permanent casing. The program has been applied to predict the axial capacities of 11 piles that were recently installed in sand and statically loaded to failure. Results from the soil mechanics procedures indicate increases, on the in situ value, of the earth pressure coefficient by up to 37%, which lies within the range 0%–100% recommended in the literature. The best CPT-based prediction method applied yields a mean (µ) and coefficient of variation (COV) of predicted to measured pile head capacity (Puh(p)/Puh(m)) of 0.83 and 0.12, respectively. Scale effects are shown to be nominal for the cases analysed. An improved method is recommended, which yields µ = 1.00 and COV = 0.10, implying higher accuracy and reliability compared with the other methods.Key words: piles, cone penetration test, static and dynamic load test, modular program.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".