Effect of SPT Hammer Energy Efficiency in the Bearing Capacity Evaluation in Sands
Bibliographic record
Abstract
Geotechnical investigation plays a vital role in all projects as the subsurface soil conditions at the project site determine the required site preparation and foundation sizes.Data from Standard Penetration Testing (SPT), the most common form of field testing in geotechnical investigations, is often correlated to soil properties for the evaluation of Net Allowable Bearing Capacity (NABC).The NABC of the project site is critical for the project as this value determines foundation sizes and whether soil improvement is required.The energy efficiency of an SPT hammer is fundamental to obtaining the correct SPT test data.While older SPT hammers, which are manually operated, may have an energy efficiency close to 60%, newer SPT hammers are often automatic and have much higher energy efficiencies.As the majority of correlations between SPT data and soil properties, which lead to NABC in geotechnical engineering, were developed based on SPT data from manual SPT hammers with energy efficiencies close to 60%, it is mandatory to normalise SPT data from higher energy efficiency hammers to that from 60% energy efficient hammers.The practice of using SPT test data without proper energy efficiency correction still exits around the world.Such use of SPT test data leads to unreliable soil bearing capacity and, in case of automatic SPT hammers, this can lead to increased project cost due to larger foundations and unnecessary soil improvement costs.This paper presents insight into such issues and how to ensure correct use of SPT test data and evaluation of soil bearing capacity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".