Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations
Bibliographic record
Abstract
In this study, an effective intelligent system based on artificial neural networks (ANNs) and a modified rat swarm optimizer (MRSO) was developed to predict the ultimate bearing capacity of shallow foundations and their optimum design using the predicted bearing capacity value. To provide the neural network with adequate training and testing data, an extensive literature review was used to compile a database comprising 97 datasets retrieved from load tests both on large-scale and smaller-scale sized footings. To refine the network architecture, several trial and error experiments were performed using various numbers of neurons in the hidden layer. Accordingly, the optimal architecture of the ANN was 5 × 10 × 1. The performance and prediction capacity of the developed model were appraised using the root mean square error (RMSE) and correlation coefficient (R). According to the obtained results, the ANN model with a RMSE value equal to 0.0249 and R value equal to 0.9908 was a reliable, simple and valid computational model for estimating the load bearing capacity of footings. The developed ANN model was applied to a case study of spread footing optimization, and the results revealed that the proposed model is competent to provide better optimal solutions and to outperform traditional existing methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".