Deterministic models for assessing productivity and cost of bored piles
Bibliographic record
Abstract
The assessment process of productivity and cost of bored pile construction is dictated by unseen subsurface obstacles, lack of contractor experience and site planning. These problems complicate the estimator's role in evaluating pile equipment productivity and cost. Current research discusses the assessment of piling process productivity and cost using the deterministic technique. Data are collected through questionnaires, site interviews and telephone calls to experts in various construction companies. Many variables have been considered in the piling construction process, such as pile size, depth, pouring method, soil type and construction method. Five deterministic models have been designated to assess productivity, cycle time and cost. The developed models are validated whereas 79% of the outputs have been predicted with more than 75% accuracy. Consequently, three sets of charts have been developed to provide the decision‐maker with a solid planning, scheduling and control tool for piling projects. If a pile has 60′ depth with φ‐18 (18″ diameter pile) in clay soil using a 5′ auger height, the cycle time is estimated as 56 and 65.5 minutes; however, productivity is 6 and 5 holes/day for dry and wet methods, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".