Uncertainty and Prediction Intervals of New Machine Learning Approach for Non-Destructive Evaluation of Concrete Compressive Strength
Bibliographic record
Abstract
This paper presents a machine learning (ML) model for predicting concrete strength using a combination of two non-destructive testing (NDT) methods: ultrasonic pulse velocity (UPV) and rebound number (RN). The model was developed using an extensive and diverse dataset and is the first such model to consider the effect of three different sample types: cubic, cylindrical, and core samples. This study is also the first of its kind to present an in-depth analysis of the results to quantify model uncertainty, which is an important prerequisite for its use in practice. Accordingly, two ML models were trained using 620 data points from the aforementioned sample types. The prediction intervals and associated uncertainties of the ML-based approach were thoroughly examined. Validation with the testing dataset showed that 93% of the testing data points for the combined cylindrical and cubic dataset fell within the 95% prediction interval, indicating strong alignment with expected results. Based on the findings, a roadmap is also proposed for future work.
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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".