EXPLORING EFFECT OF DIFFERENT RESOURCE QUALITIES ON PROCESS EFFICIENCY IN CONSTRUCTION PILE INSTALLATION
Bibliographic record
Abstract
This paper presents the measured effects of different resource qualities on construction performance. The paper describes a recommended method, proposed with the concept of prediction by understanding the causal effect of process resources on consequent work efficiencies. The project team measured and compared the different arrangements of resources and their effects on on-site work efficiencies. The paper includes a field study of 15 operations (40 piles) in Melbourne, on several worksites of prefabricated piles and installations. It aimed to determine the causality between the set of delivered prefabricated piles and relevant work efficiencies. This field includes its purpose of generating and providing scientific evidence in effectively implementing an offsite operation. One of the critical factors affecting the efficiency of the installation process was confirmed to be the location of the longest section in the sequence. It took 21.8 minutes longer with the middle part of the installation if the longest section was designed to be in the middle of the whole prefabricated steel pile. The findings confirmed the need for holistic communication along the supply chain. The originality of this project is to provide a case study that offers archival evidence of the proposed model in a practical situation.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".