Probabilistic Assessment of Seismic Bearing Capacity of Strip Footings Seated on Heterogeneous Slopes Using Finite Element Limit Analysis (FELA) and Response Surface Method (RSM)
Bibliographic record
Abstract
The paper demonstrates the use of the response surface method (RSM) to carry out probabilistic assessment of the seismic bearing capacity of shallow footings seated near naturally occurring heterogeneous slopes.To this end, a pseudo-static loading is applied to a randomly uniform slope, which is homogeneous in each case but random between realizations.The method substantially reduces the number of Monte Carlo simulations required to carry out cumbersome probabilistic slope stability analyses.A finite element limit analysis model based on the lower bound theorem is developed, which is then used to generate a large synthetic database of numerical results for the seismic bearing capacity of shallow foundations resting on inherently variable natural slopes.To this end, a permutation of the key parameters is formed and lower bound FELA-based limit loads are sought through optimization in MATLAB.A closed-form solution is formulated using RSM-based polynomials.The RSM equations, which are acquired from least squares regression analyses, are used to carry out probabilistic Monte Carlo simulations and the results are presented in forms of cumulative distribution functions.Results from the probabilistic analyses are introduced into some reliability-based design approach to render design loads for different reliability levels.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".