Influence of Hammer Energy Correction on SPT Correlations and Interpretation
Bibliographic record
Abstract
Standard Penetration Test (SPT) is a widely used geotechnical exploration test.SPT is easy to perform and is cost-effective; hence it has become prevalent.Several factors affect the test results, i.e., the number of blows (N-value) for penetration of the sampler into the soil at any given depth.Among those factors, hammer energy (𝐸𝐸 𝐻𝐻 ) is the most important.Even though it is essential, there have been limited studies considering the influence of 𝐸𝐸 𝐻𝐻 on the determination of different subsurface properties correlated with N-values.Several empirical relations have been developed between N-value and static and dynamic subsurface properties which however, can be used only to a particular region and a specific 𝐸𝐸 𝐻𝐻 value.The influence of considering proper in-situ hammer energy in these correlations is not clearly understood yet, and thus it is still not practised in many developing countries.This study highlights the importance of hammer energy in N-value corrections and studies the effect of hammer energy on soil properties like low strain shear modulus and state of denseness.The influence of different SPT corrections is studied, along with the impact of including energy measurements in analyzing the correlation between the SPT N and low-strain shear modulus.
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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".