The Role of Full Scale Testing in ASD and LRFD Driven Pile Designs
Bibliographic record
Abstract
For design of foundations on driven piles, Load and Resistance Factor Design (LRFD) is increasingly replacing the conventional Allowable Stress Design (ASD) approach. LRFD separates the uncertainty in loading conditions from the uncertainty in resistances, while the ASD approach uses a single "global factor of safety" to cover all uncertainties. Concerning resistances, LRFD uses a range of "resistance factors" adjusted to the method of capacity evaluation and level of quality control and assurance during construction, while the previous ASD approach often considers only the method of capacity evaluation but not the amount of testing. Considerable confusion exists as to how to best apply the new LRFD methodology and reap its potential benefits to affect a safe and economical foundation. This paper attempts to address some of the issues and answer questions associated with the newly implemented method. The benefits of full scale pile testing, by either static or dynamic methods, and of an increased amount of such testing, are illustrated by numerical examples. After a review of the global factors of safety for different codes, pile designs using ASD global factors of safety are compared with designs produced using the most recently developed LRFD resistance factors from AASHTO Specifications. Among the capacity evaluation methods will be static analysis, dynamic formula, wave equation, dynamic testing and static testing. Although the prime emphasis is the AASHTO specification, comparisons are also made with resistance factors from current standards from Europe, Australia, and Canada.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".