Neurofuzzy-Based Productivity Prediction Model for Horizontal Directional Drilling
Bibliographic record
Abstract
Productivity prediction and cost estimation of horizontal directional drilling (HDD) as a trenchless technology technique involves a large number of objective and subjective factors, which should be carefully identified and studied. To consider the effect of these factors on productivity prediction, the research presented in this paper assists in developing a productivity model for HDD operations. Potential factors impacting productivity are identified and studied based upon the literature and HDD experts across North America and abroad. A neurofuzzy (NF) approach is employed to develop the HDD productivity prediction model operating in clay, rock, and sandy soils. The merits of this approach involve decreasing uncertainties in results, addressing nonlinear relationships, and dealing well with imprecise and linguistic data. The NF model is tested using actual project data, which showed robust results with average validity percentages of 94.7, 82.3, and 86.7% for clay, rock, and sandy soils, respectively. The model is also used to produce productivity curves (production rate versus influencing factors) for each soil type. An automated user-friendly productivity prediction tool (HDD-PP) is developed to predict HDD productivity based on the NF model. This analysis has proved helpful for contractors, consultants, and HDD professionals in predicting execution time and estimating cost of HDD projects during the preconstruction phase.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".