Predicting the Fundamental Period of Light-Frame Wooden Buildings by Employing Bat Algorithm-Based Artificial Neural Network
Bibliographic record
Abstract
The study utilizes an artificial neural network model for determining the fundamental period of Light-Frame Wooden Buildings, employing the Bat algorithm on a data set of 71 measured periods of wooden buildings. The number of stories, floor area, storey height, maximum length, and maximum width are selected as input parameters to estimate the fundamental period of light-frame wooden buildings. The accuracy and the competitiveness of the developed model were evaluated by comparing it with a similar particle swarm optimization (PSO)- ANN scheme, the formulas provided in the National Building Code of Canada, an equation obtained from the Eureqa software, and a non-linear regression (NLR) model. The results of the research show that the bat-ANN model exhibited the best overall performance with the lowest RMSE and MAE error values and the highest values of the Coefficient of determination, R2, in comparison to the other examined models. Therefore, the proposed Bat-ANN model can be considered as a reliable, robust, and accurate tool for predicting the fundamental period of wooden buildings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".