Instance-Based Model for Predicting Total Fabrication Duration of Industrial Pipe Spools
Bibliographic record
Abstract
Industrial fabrication for modular installations has its own set of challenges that combine the environments of industrial manufacturing and off-site construction. This hybrid execution strategy means that fabricators need to look at both fields and adopt the best tools and techniques. This thesis presents an investigation into the improvement of delivery time estimates of industrial fabrication of a pipe spool fabrication shop in Alberta. The main contribution of the work is in the area of predicting the total fabrication duration to be expected in order to assist fabrication shop management in planning for appropriate workforce availability and material delivery date requirements. In order to address the objective of improving the prediction of fabrication durations, the spool manufacturing process has been modelled for simulation. However, the data required to validate this model was found to be time-consuming and cumbersome to capture at the required level of details. Alternatively, the development of a data-driven knowledge discovery experiment was pursued. The approach employed was to utilize the fabrication information that was already being captured by the manufacturing facility and evaluate it using instance-based classification. In addition, an effort towards the integration of manufacturing tracking and scheduling estimating is presented. This part of the work will ensure that the schedule is not consulted only at the beginning of a project, but throughout its completion. Updating a schedule with live fabrication progress data will allow production managers to update their completion date estimates and adjust the manufacturing plans to reflect existing issues such as material delivery and labor shortages or performance.
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 | 0.001 |
| 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".