Automated Mathematical-Based Design Framework for The Selection of Rigging Configuration
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Bibliographic record
Abstract
Automated Mathematical-Based Design Framework for The Selection of Rigging Configuration Seyed Mohammad Amin Minay Hashemi, Sanghyeok Han, Jacek Olearczyk, Ahmed Bouferguene, Mohamed Al-Hussein and Joe Kosa Pages 172-178 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Modularization in construction involves erection of large and heavy prefabricated modules at the job site. Modules, especially in industrial plants, are required to be lifted without any tilted angles vertically and horizontally to prevent applying bending moments to the lifting lugs and structural components. Configuration of rigging elements, which are the link between the crane hook and the module, plays a vital role in the load distribution to the rigging components. In practice, designing a rigging assembly to ensure safe and successful lifts is a time-consuming and tedious process relying heavily on guesswork, especially when the modules center of gravity is offset. In addition, the pitch angle of the module remains unknown until it is lifted, thus raising safety issues regarding the failure of rigging components. To overcome these limitations, this paper proposes a mathematical-based design framework which consists of: (1) collecting the module information; (2) designing a preliminary configuration by selecting the rigging components from the database; (3) Optimizing the number, size and capacity of the rigging components selected for the preliminary configuration in order to ensure that positions of module and spreader bars are set on parallel lines without tilted angles; and (4) reporting the list of used rigging components and visualizing their configuration as the output. To validate this framework, this paper uses a case study which designs the optimal rigging configuration for a 4-point pick module based on the inventory availability. Keywords: Crane rigging; Automation; Center of gravity offset DOI: https://doi.org/10.22260/ISARC2019/0024 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.000 |
| 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