Estimating the In-Plane Lateral Resistance of Reinforced Log Wall Employing Soft Modelling Techniques
Bibliographic record
Abstract
The popularity of log houses has been on the rise in numerous regions worldwide. In the context of log construction, the stability of log walls is notably influenced by the friction existing between the layers of logs and the openings designated for windows and doors. This study endeavors to comprehensively evaluate the lateral resistance of log walls through an extensive parametric analysis utilizing finite element (FE) methods. To construct a robust dataset, a total of 71 distinct samples were generated employing FE analysis, where the shuffled frog-leaping algorithm (SFLA) was incorporated in conjunction with a feed-forward (FF) neural network. Within this framework, the accuracy of the SFLA-based informational model was juxtaposed against that of an artificial neural network (ANN) coupled with particle swarm optimization (PSO), genetic algorithm (GA), and statistical models including multiple linear regression (MLR).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".