Effect of Data Division on Classification Performance Model Prediction of Specified Compressive Strength Core Concrete Using Ultrasonic Pulse Velocity in Tandem with Machine Learning
Bibliographic record
Abstract
The accurate assessment of concrete quality and structural integrity is of paramount importance in the field of civil engineering.Non-destructive measurement is the best option for assessment because it will be efficient and does not require damaging existing structures.One non-destructive technique that has gained significant attention is using Ultrasonic Pulse Velocity (UPV) measurements in conjunction with machine learning algorithms to classify core concrete.This study aims to predict the classification of specified compressive strength core concrete using UPV in tandem with machine learning affected by data division.The investigation explores how different data partitioning techniques, such as random splitting sampling (90/10, 80/20, 70/30, 60/40, 50/50), influence the accuracy capability of the classification models.Random splitting sampling technique data was chosen because this method is the most common and frequently reported in previous research.This study uses machine learning algorithms, including Linear Discriminant Analysis (LDA), K-Nearest Neighbor (kNN), Decision Tree (DT), and Random Forest (RF).By systematically evaluating the effect of data division on model performance, this research contributes to refining concrete quality assessment methodologies.It advances the understanding of the synergy between nondestructive testing and machine learning.The results of this study indicate that the model developed by the kNN algorithm is the best and most robust against data division in classifying compressive strength core concrete using Ultrasonic Pulse Velocity.The performance of this machine learning algorithm model through accuracy in calibration and validation in all data splitting is between 0.98 and 1.00.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".