Behavior Analysis of High-Performance Concrete Using Data Mining Techniques
Bibliographic record
Abstract
This study tried to predict mechanical behavior of high-performance concrete (HPC), specially the compressive strength of HPC using different data mining methods. HPC is a highly complex composite material and modelling of its dynamics is a real challenge. Moreover, compressive strength of HPC is nonlinear function of its ingredients. The results of several studies have represented that compressive strength of HPC depends on not only water/cement ratio but also some other additive ingredients. It is actually a function of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age. The quantitative analysis in this study were conducted by using Principal components analysis (PCA) and Multiple Regression (MR) methods. For this purpose, some effective statistical analysis and modeling software such as MATLAB, MINITAB and R has been used. Analytical results suggested that Multiple Regression is effective for predicting behavior of HPC based on its compressive strength with respect to different ages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".