The Effect of Setup Time in Geotechnical Resistance Factor of Driven Steel Piles in Alberta: a Case Study
Bibliographic record
Abstract
Determining the bearing capacity of driven steel piles is a critical concern in geotechnical engineering particularly when constructing major structures in oil and gas and infrastructure projects in Alberta. To verify pile capacity, the pile driving analyzer (PDA) and static load tests (SLTs) are widely used. In recent years, PDA testing has become a regular part of pile quality assurance programs on projects and increasingly used as full-scale load tests. Several studies have shown that geotechnical resistance factor (GRF) values can be calibrated with the aim of PDA testing results which can help designers to potentially reduce the number and length of piles. Nevertheless, uncertainty remains regarding the extent to which the GRF value can be optimized following the implementation of the PDA. In this paper, a database of PDA tests and SLTs are compiled from several projects in Alberta, Canada. The primary objective of this study is to assess the impact of time on the recommended GRFs by several codes. To achieve this, the study employs a well-established probabilistic technique known as Monte Carlo Simulation (MCS) to quantify the influence of time, referred to as “setup time”. To enhance the assessment of setup time’s effect, the recorded bearing capacities are collected during two distinct time points: after the completion of pile installation or the end of drive (EOD) condition, and after a specific setup time at the Beginning-of-Restrike (BOR) condition. The results suggest that by taking into account the time impact, GRF values can be optimized, leading to an increase in factored pile resistance and ultimately resulting in a more cost-effective design process for steel-driven piles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".