Novel toe driving for thin-walled piles and performance of fiberglass-reinforced polymer (FRP) pile segments
Bibliographic record
Abstract
Despite the rapidly growing use of pile foundations, it is presently difficult to assure the integrity and uniformity of the cross-sectional area of cast-in-place piles when using normal concrete. Cavities and soil encroachments leading to soil pockets can jeopardize their load-bearing capacity. Moreover, corrosion in reinforced concrete and steel shell piles has been very costly, exceeding US$2 billion in annual repair costs in the United States alone. To address these two challenges, extensive research has been underway at the University of Western Ontario to develop novel technology for the construction of piles. Self-consolidating concrete (SCC), a material that flows under gravity and assures the integrity of piles, is cast into fiberglass-reinforced polymer (FRP) tubes that provide corrosion-resistant reinforcement. A toe driving technique was developed to install the empty FRP shells into the soil, and SCC is subsequently cast into the shells. Driving tests using this new technique were carried out on large-scale model FRP and steel pipe piles installed in dense dry sand enclosed in a pressure chamber. FRPSCC and steel closed-end piles were also driven using conventional piling at the pile head. Static load tests were conducted on the various pile specimens under different vertical and horizontal confining pressures. The pile specimens were instrumented to investigate their dynamic behaviour under driving and their response to static compressive, uplift, and lateral loading. It is shown that the toe driving technique is very suitable for installing FRP piles in dense soils. Results from the driving tests and static load test indicate that FRPSCC hybrid piles are a very competitive and attractive option for the deep foundations industry.Key words: FRP, self-consolidating concrete, piles, pile drivability, toe driving, axial load, uplift load, lateral load, large-scale modeling, shaft resistance, dense sand.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".