A framework to optimize a designed geotechnical system probabilistically using MLP-ANN and ELECTERE decision making – a nailed wall study
Bibliographic record
Abstract
This research presents a comprehensive framework for the optimization of a designed geotechnical systems probabilistically, combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the ELECTRE decision-making method. By incorporating the Single Random Variable (SRV) approach and Latin Hypercube Sampling (LHS), the framework is adaptable to various geotechnical applications. A case study on a nailed wall system builds on prior research, optimizing critical design factors such as nail length (L), diameter (D), and horizontal spacing (S h ). The optimization aims to maximize the Factor of Safety (FS) while minimizing maximum horizontal displacement (H max ), maximum moment (M max ), and maximum shear force (V max ). To improve optimization efficiency, a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) is utilized. The SRV approach generates cumulative distribution functions (CDFs) for the objectives, with Conditional Value at Risk (CVaR) applied at a 95% confidence level to address extreme event risks. The results are ranked using the ELECTRE method, providing valuable insights into selecting Pareto-optimal solutions based on the importance of different objectives. This framework enhances reliability and safety under uncertainty and can be adapted to a range of geotechnical system designs. Step-by-step framework for optimization of a designed geotechnical system under uncertainty
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".