Prediction of soil type and standard penetration test (SPT) value in Khulna City, Bangladesh using general regression neural network
Bibliographic record
Abstract
In this study, a general regression neural network (GRNN) is developed for predicting soil type and standard penetration test (SPT) N (standard penetration resistance) values based on SPT test results. It focuses on soils mainly in Khulna City, Bangladesh that comprise fine-grained alluvium deposits of mostly silt and clay with some organic content and sand. A detailed geological and geotechnical investigation of the city and its surroundings was conducted to generalize the subsoil condition of the study area based on soil type and SPT values. The investigation results showed that the city is divided into four geological units and three geotechnical zones. To develop the GRNN model, more than 2326 field SPT values ( N ) have been collected from 42 clusters containing 143 boreholes spread over an area of 37 km 2 . Two trained models were developed: initially the borehole locations were trained with the soil types and after that the borehole location-soil types were trained with the N c values. The model prediction was compared with the borehole data and the results showed that the GRNN model predicts well compared with the actual site investigation data. Therefore, this model can be used for future planning and expansion of the city.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".