Bibliographic record
Abstract
With the rapid increase of new installations, replacement and repairs of pipe utilities, the demand for trenchless excavation methods with minimum disruption to the public such as horizontal directional drilling (HDD) has increased. Canadian National Research Council reports that rehabilitation of municipal water systems would cost $28 billion from year 1997 to 2012 (NRC 2004). Contractors, engineers, and decision makers are always facing a challenge of how to estimate the cost of new pipe installation using the HDD due to the presence of subjective factors. The HDD process involves a large number of factors to be considered for productivity prediction and cost estimation. Therefore, an emergent need for developing a dedicated HDD productivity model is currently undertaken to meet industrial needs. The presented research aims at identifying the main factors that affect productivity of HDD operations and designing a productivity model. A neurofuzzy approach is utilized to design the HDD productivity prediction model for underground pipe installations in clay soil. The neurofuzzy system is developed based on actual project data that are collected through interviews, phone calls and questionnaire surveys. Results show that crew and operator skills and pipe diameter greatly affect the HDD productivity and the project as a whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".