Artificial Neural Network Models for Predicting California Bearing Ratio of Lateritic Soil Admixed with Reinforce and Rice Husk Ash
Bibliographic record
Abstract
California bearing ratio (CBR) is an indispensable parameter in the design of road pavement, repeated carrying out of this test has been chiefly monotonous and time wasting, also the use of cement as stabilizer has also been increasingly expensive, hence, the need for admixing with agrowaste ash such as rice husk ash (RHA).This research is carried out for the prediction of the CBR of lateritic soil admixed with cement and RHA by means of an artificial neural network (ANN).Six parameters are selected as input variables to obtain results that are accurate and precise.The six input variables are cement, RHA, liquid limit, plasticity index, maximum dry density and optimum moisture content, while CBR Unsoaked and CBR Soaked are the output variables.The study consists of a database of 1288 samples obtained from laboratory experiments which were subdivided into 70% for training, 15% for testing, and 15% for validation.The training operation is performed by a multilayer perceptron-back propagation algorithm.The network topology is achieved after fixing a number of hidden neurons.Thereafter, statistical indices are used in evaluating the performance of the ANN model.It is established that this model is appropriate for accurate prediction of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".