Prediction of standard penetration test <i>N</i>-value from dynamic probing light <i>N</i>-value using ANFIS and multiple regression models
Bibliographic record
Abstract
Standard penetration test (SPT) is one of the most widely used tools to predict the soil properties. In recent years, the dynamic probing light (DPL) test has been performed more frequently for geotechnical applications because it is more cost-effective and fast. Since the majority of empirical equations of soil properties are related to SPT N-value, it is beneficial to find the best correlation between SPT and DPL N-values. In this study, the adaptive neuro-fuzzy inference system (ANFIS) and multiple regression (MR) are used to predict the correlation between SPT and DPL N-values. To achieve this goal, the soil properties of 64 sample specimens of silty clay were calculated at various depths. Three parameters including depth, total density, and DPL N-value were chosen as the input of the models. Results show that both methods estimate the correlation between SPT and DPL N-values precisely. However, ANFIS predicts more accurately than multiple regression.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".