Bibliographic record
Abstract
Purpose – Tight schedules in high-rise building construction force project managers to use the formwork even in a bad weather condition. Insufficient craning, which is typically the bottleneck in construction activities, and lack of space in confined sites make it hard to install the formwork on the ground. The Automatic Climbing System (ACS), a type of jump forms, solves these problems enabling the formwork to climb in various weather and height conditions. The aim of this paper is to discuss these issues. Design/methodology/approach – Current research focusses on the ACS, its application, and productivity assessment. Productivity and construction data are collected from a specialized company in such type of forms. A bracket productivity model has been developed to estimate floor construction cycle time and productivity. Findings – Results show that average productivity is four days/floor. The developed model is validated, which shows robust results 97.80 percent. Research limitations/implications – The implementation of the developed models are limited to only two projects. However, the developed models and framework is sound for future improvement. Practical implications – The developed methodology and model play essential roles in decision-making process. Originality/value – The developed methodology and model are beneficial to researchers, practitioners, and planners of construction projects. It provides practitioners with charts that assist in scheduling and managing resources for jump form application. In addition, it provides researchers with a floor cycle time model and framework of implementing jump forms to high-rise buildings.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".