Application of Least Squares Support Vector Regression Analysis to Green Concrete with Oil Palm Husk Aggregate
Bibliographic record
Abstract
Least Squares Support Vector Regression (LSSVR) machine has the advantages of small sample, nonlinearity and high dimensionality, which can solve the problem of predicting the compressive strength of green concrete with oil palm shell aggregate.In this paper, the error sum of squares instead of the error sum is used as the objective function, IFFA is used to find the optimization of the kernel function parameters and penalty factors of LSSVR, and the PWLCM-based chaotic search is used to initialize the population, and ultimately the improved auricular fox algorithm is realized for the optimization of the least squares support vector regression algorithm, which makes it have strong fitting and generalization abilities, and significantly reduces the burden of computation, thus improving the Computational efficiency.Application of the designed combined algorithm for compressive strength prediction of concrete reveals that the R², MAPE and RMSE values obtained by this paper's model on the training dataset are 98.71%, 5.92% and 1.0823 MPa, respectively.The correlation coefficients predicted by the model are much closer to 1 as compared to that of the baseline model, which suggests that this paper's model possesses a superior generalization capability, making it more effective in dealing with complex and invisible data.The adopted method is practical and innovative, and is of guiding significance for practical engineering.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".