Utilization of a Bayesian Network Algorithm to Predict the Compressive Strength of Concrete
Bibliographic record
Abstract
Artificial Neural Network (ANN), Fuzzy Logic (FL), and Random Forest (RF), have been utilized widely in past literature in predicting the behavior of concrete products. Few studies used the probabilistic inference approach through Bayesian Networks (BN) to envisage the structural health integrity of concrete, while the possibility of employing BN algorithms for the prediction of its mechanical properties has not been investigated yet. This research evaluates the potential applicability of BN in predicting the compressive strength of selfcompacting concrete made with various supplementary cementitious materials and basalt fibers. Two learning algorithms, namely Nave Bayes and Markov Blanket, were employed along with various discretization methods to maximize network performance and minimize integral absolute error. Research findings showed that the Nave Bayes classifier coupled with K-means discretization tool with 4 segments of 'days' data and 3 segments of the remaining variables gave the highest correlation between experimental and predicted values. Meanwhile, the Markov Blanket algorithm failed to accurately predict the compressive strength. The accuracy of the predicted BN was found to be comparable to that obtained from an ANN model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".