Sequential dependency structure matrix based framework for leveling of a tower crane lifting plan
Why this work is in the frame
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Bibliographic record
Abstract
Recent construction projects involving the building of extremely tall and large structures have increased the demand for various major equipment, including tower cranes (TCs). However, most lifting plans for TCs at construction sites are performed based on the experience and intuition of the site manager as opposed to a systematic process of rational work. This study presents a framework for scheduling TCs using sequential characteristics of a dependency structure matrix (DSM) to efficiently improve the lifting plan of TCs. In this research, a real world construction case study involving a TC in charge of three buildings was examined. The results of the case study indicated that the scheduling of TC using sequential DSM was useful in leveling the TC lifting plan in terms of ease of use, especially in the typical floor cycle lifting planning. Therefore, the TC lifting plan based on sequential DSM presents a more precise and systematic TC lifting plan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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