A decision support tool for planning module installation in industrial construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Properly planned module installation on an industrial site is a critical factor in delivering a project safely, on time and within budget. Different sizes of heavy-duty mobile cranes are used to pick, swing and place the modules. Crane selection depends on module size and weight, as well as crane availability, location and configuration. Weeks can be spent in trial and error to prepare and improve module installation plans due to the large number of ways to install the modules on site, high crane operating costs and other crane-module constraints. A tool to automatically generate module installation plans is essential. Design/methodology/approach This paper presents a novel heuristic-based methodology for planning and sequencing module installation on industrial construction sites that takes into account proposed technological constraints. Findings Case studies are presented to demonstrate the ease and effectiveness of the developed methodology in planning module installations. Originality/value On a complex project, the tool can save time in preparing the installation plan, while also reducing the amount of crane supporting tasks (foundation preparation and crane movement).
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.
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 it