A Predictive Model For Uniaxial Compressive Strength of Carbonate Rocks From Schmidt Hardness
Bibliographic record
Abstract
Uniaxial compressive strength (UCS) is considered to be one of the important parameters in rock engineering projects. In order to determine UCS, direct and indirect techniques are employed. In the direct approach, UCS is determined from the laboratory UCS test. In indirect techniques determine UCS based on the nondestructive test findings which can be easily and quickly performed and require relatively simple or no sample preparation. Indirect techniques are commonly preferred by rock and mining engineers because of their low cost and ease. This study presents the findings of an Artificial Neural Networks (ANN) based model for the prediction of UCS from Schmidt hardness. Schmidt hardness test (SHT) is a nondestructive test method which provides fairly good correlation about the strength of rocks. SHT can be easily and quickly conducted with a portable device known as Schmidt Hammer and it does not require any sample preparation. ANNs have been widely used in solving engineering problems and have emerged as powerful and versatile computational tools for organizing and correlating information in ways that have proved useful for solving certain types of problems too complex, too poorly understood, or too resource-intensive to tackle using more traditional numerical and statistical methods. For this reason, ANNs are used in this study to predict UCS of carbonate rocks from the Schmidt hardness rebound value (N R). A set of 37 test measurements obtained from 37 different carbonate rocks (marble, limestone, and travertine) are used to develop the ANN-based model. The results of the ANN model were also compared against the results of a regression model. The criteria used to evaluate the predictive performances of the models were the coefficient of determination (R 2), root mean square error (RMSE), and variance account for (VAF). The R 2, RMSE, VAF indices were calculated as 0.39, 46.51, 12.45 for the regression model and 0.96, 7.92, 95.84 for the ANN model, respectively. The results show that ANN-based model produces significantly better results than the regression model. It was concluded that the N R value is a useful indicator for the prediction of UCS from the ANN model developed in this study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".