Calibration of Geotechnical Resistance Factor for Axially Loaded Steel Piles Driven in Alberta and Related Cost-Benefit Analysis
Bibliographic record
Abstract
Driven steel open-ended piles are used as foundations to support structures by transferring loads to the surrounding soil and/or deeper, more competent strata. These foundations are commonly employed in oil & gas, and infrastructure projects across Alberta. During the design phase, their capacity is typically estimated using semi-empirical equations based on static analysis methods and soil mechanics principles. However, uncertainties in pile capacity prediction persist, primarily due to site characterization, soil behavior, and construction quality. To address these uncertainties and ensure adequate safety under working loads, a Geotechnical Resistance Factor (GRF), as recommended by part of LRFD design approach, is applied to the calculated pile capacity. In Canada, based on the updated Canadian Foundation Engineering Manual (CEFM) for sites with a typical level of geotechnical understanding and where neither Pile Dynamic Analysis (PDA) nor static load testing is performed, a GRF with a value of 0.4 is recommended. However, several studies have indicated that using a uniform GRF without accounting for regional soil and bedrock variability often results in overly conservative designs. Similarly, applying a single GRF value to both shaft and toe capacities, while neglecting pile setup effects, further compounds this conservatism. Recalibration of GRFs is therefore necessary to better reflect these variables. This study aims to recalibrate GRF values regionally within Alberta for open-ended driven steel piles that have been axially loaded with the use of advanced statistical methods, PDA test data collected across the province and related geotechnical investigation data. The regional calibrated GRFs have been obtained initially for five (5) regions of Alberta (NE, NW, SE, SW, C). These values have been further recalibrated considering pile setup effects and differentiate value for shaft and toe capacities. Additionally, a cost-benefit analysis has been used to evaluate how much optimized GRFs can significantly reduce pile length requirements in design and cost in construction. Results show that locally calibrated GRFs yield the greatest savings about 20% compared to 10% for regionally calibrated values and 15% for soil-type-based calibrations. Incorporating pile setup effects into locally calibrated GRFs further enhances cost efficiency, with potential savings ranging from 23% to 35%, depending on soil conditions and calibration methodology.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".