Assessment of Driven Pile Ultimate Capacity through Artificial Neural Network Analysis of Cone Penetration Test Data
Bibliographic record
Abstract
In this research, the application of an artificial neural network (ANN) was employed utilizing cone penetration test (CPT) information to produce an enhanced comprehension of the ultimate load-bearing capacity of piles. The ANN algorithm is independent of correlation assumptions as it uses prior cases/instances to grasp the relationship. A database of eighty pile load tests on squared precast/prestressed concrete (PPC) driven piles and corresponding CPT data was prepared in this regard, in which the ANN models were trained using these data. Feed-forward network techniques such as backpropagation algorithm, Levenberg–Marquardt algorithm were used with trial and error. The cone sleeve friction and corrected cone tip resistance were used to train numerous ANN models. A comparison was made between the prediction of ANN models and three pile-CPT methods, that is, Laboratoire central des pontes et chaussées (LCPC), probabilistic, and University of Florida (UF) methods. The findings of this research exhibited that ANN excels in the evaluation of ultimate capacity of squared PPC piles. A comparison was also made with LCPC, probabilistic, and UF method on the basis of reliability-based load and resistance factor design analysis, which also demonstrates higher resistance factors, ϕ, and superior efficiencies of ANN models over the traditional pile-CPT methods. Consequently, these discoveries reinforce the efficacy of utilizing ANN for assessing the ultimate load-bearing capacity of piles through the interpretation of CPT data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".