Reliability-based Design for Axially Loaded Driven Piles in Glacial Deposits
Bibliographic record
Abstract
Many factors and uncertainties, especially from the soil conditions, challenge the accuracy of a pile design for the geotechnical ultimate limit state (ULS) and serviceability limit state (SLS). The uncertainties can be contributed by the inconsistent material properties of glacial deposits or a lack of provisions by many design methods for the pile set-up, which is an increase in the pile capacity with time. Thus, for driven steel piles subjected to static and maintained axial loads, the goal of this research was to improve the predictability of these limit states by modifying reliability-based design (RBD) methods. A database was collected first in this thesis with a total of 120 piles subjected to static and/or Pile Driving Analyzer (PDA) tests. These tests were used to evaluate the accuracy of several existing design methods that predict the pile capacity with in-situ measurements, particularly by the standard penetration test (SPT) or piezocone penetration test (CPTu). Since existing methods commonly overestimated the capacity or exhibited significant variations in their predictions, correlations were conducted between results from the in-situ and pile tests to develop new design methods to better consider the soil content and set-up time. Many existing set-up prediction methods rely on time-consuming laboratory tests, but this study offers a practical CPTu-based approach. Afterwards, reliability analyses were performed to help designers account for the uncertainties in designs and estimate the factored resistance for the ULS and SLS. Although a few studies have previously incorporated set-up into RBDs for the ULS, very few, if any, have incorporated set-up for the SLS; thus, this thesis presents a series of resistance factors that were calibrated by Monte Carlos simulation for a range of set-up to end-of-driving resistance ratios and allowable settlements. The findings demonstrate the benefits of versatile methods that consider a range of conditions, such as set-up time, soil classification, and pile geometry. In addition, economic benefits may be received for designs by using a greater factored resistance at a set-up time compared to the end-of-pile-driving condition. Overall, the findings can assist practitioners to mitigate geotechnical risks and deliver more economic and reliable pile designs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".