The influence of soil layering and penetrometer diameter on penetration resistance
Bibliographic record
Abstract
Methods employing cone penetration test (CPT) data for site characterisation and foundation design have continued to evolve as use of the CPT grows worldwide. Research to assist the development of such methods has included penetration testing in a laboratory environment where the presence of thin soil layers in samples has highlighted the need for improved understanding of the influence of penetrometer size. This paper presents the results of a systematic experimental investigation of the relationship between the penetration resistance, penetrometer diameter, and relative strength of the soil layers in two-layered sand–sand and sand–clay profiles. The results combined with other high quality experimental results reported in the literature are used to quantify the influence of the strength of the layers on the cone resistance at the boundary of two layers as well as on the nature of the sensing and development sections of the cone profile. These observations inform modifications to the Boulanger and DeJong filtering method, with the primary objective of developing a consistent approach for the prediction of ultimate end bearing resistance of driven piles in layered stratigraphy. Examples using different diameter penetrometers in the field and in multi-layered deposits created in the laboratory illustrate the suitability of these modifications.
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 | 0.002 | 0.008 |
| 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 | 0.000 | 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".