Implementation of Artificial Neural Network for Forecasting California Bearing Ratio of Treated Cement-Laterite Soil Improved with Bamboo Leaf Ash
Bibliographic record
Abstract
Finding the California Bearing Ratio (CBR) of soil stabilised by an environmentally friendly binder composite is one of the most important steps in designing an appropriate mix.By utilising an artificial neural network (ANN) to forecast soil parameters and additions of Portland cement and Bamboo Leaf Ash (BLA), this study aims to estimate the California Bearing Ratio (CBR) of treated cement-lateritic soils.The precise and accurate findings are obtained by selecting six factors as input variables.Maximum Dry Density (MDD) (kg/m 3 ), Plasticity Index (PI) (%), Liquid Limit (LL) (%), Cement (%), Bamboo Leaf Ash (BLA) (%), and OMC (%) were the six input variables.In contrast, the output variables were CBR soaked (%) and CBR unsoaked (%).1288 samples from a database were used in the investigation.Training is done using a multilayer perceptronbackpropagation algorithm.The network topology is acquired after the fixing of several hidden neurones.With a 99.5% accuracy rate, the model can predict CBR results.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".