Role of Soil and Structural Heterogeneity in Geotechnical System Redundancy
Bibliographic record
Abstract
It is well known that redundancy generally improves system reliability. For example, a geotechnical support system comprised of a single monopile will have the same failure probability as the monopile itself. Alternatively, if the geotechnical support system is comprised of two piles, each of which can support the load with probability 1−pf, then the system failure probability will lie somewhere between pfand p2f. In this case, if the failure probability of an individual pile is pf=1/100, then the system failure probability will lie between 1/100 and 1/10,000, depending on the degree of statistical dependence between the piles. Clearly, redundancy in the geotechnical system has the potential to significantly reduce the system failure probability. From a design point of view, since redundancy generally increases system reliability, the individual system elements (e.g., piles) need not necessarily be designed to the same level of reliability. In other words, if the supported load is distributed amongst a number of footings or piles, redundancy should be taken into account to achieve construction savings while maintaining overall safety. This paper looks specifically at the effects of redundancy in pile support systems on the overall system reliability. It is assumed that the support only fails when all piles have failed and piles fail randomly according to the local ground strength (pile structural capacity is not considered). Two load transfer models between failed (excessively displaced) and surviving piles are considered. Pile system reliability is then estimated as a function of the distribution of pile resistance, the load transfer model, the number of piles, and the target design reliability of individual piles. Charts are produced to allow the selection of individual target design reliability for a given number of piles and the target system reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".