The Importance of Performance-Based Geotechnical Parameters for Nonlinear Analysis
Bibliographic record
Abstract
Traditional geotechnical engineering practice typically utilizes a wide variety of empirical correlations which contain varying degrees of epistemic and aleatory uncertainty. The source of the uncertainty varies from the use of poorly controlled field test methods to sample disturbance prior to laboratory testing. When accounting for uncertainties such as these, the geotechnical engineer may be influenced by a desire to give "conservative" values or "better" values to the structural engineer. However, when considering performance-based design, rather than accounting for the uncertainties in empirical formulae and test results through the use of poorly understood and inconsistently applied factors of safety, there is a need for a reduction in the uncertainties themselves. This can be accomplished through the use of high quality field and laboratory testing methods. It is important that the uncertainties in the geotechnical parameters are reduced and that arbitrary safety factors not be applied so that the actual performance of the structure can be accurately modeled by the structural engineer. The best estimate of geotechnical. performance is what is required. Overly conservative or overly aggressive foundation stiffness and/or bearing capacity values, which may seem desirable to some engineers, can lead to the incorrect predicted failure mechanism of the structure and result in a misguided retrofit scheme. Accurate estimates of geotechnical parameters and reduced uncertainties can help owners build safety and cost savings into the analysis and retrofitting of existing buildings. A case history is presented where performance-based design concepts were utilized in obtaining geotechnical parameters for the nonlinear analysis of an existing hospital building.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".