Applying the Flow-interception Location Model to Select the Location of Construction Industrial Yard
Bibliographic record
Abstract
Construction industrialization is a remarkable innovation and improvement in construction industry. This new construction method can address the drawbacks of conventional one, which has extensive mode of production, lower labour productivity and serious energy consumption and environmental pollution. Recent years, the construction industrial yard emerges as a method to promote the modernization of construction industry in China. However, there has been little to no empirical literature discussed the method to identify the location of construction industrial yard. The location problem is one of key factors to affect whether the yard can be sustainable in economic, social and environmental profits. In order to make the location of construction industrial yard more scientific and meet the special requirements of construction industry, this paper proposes a bi-level model based on the flow-interception location model (FILM). In the location model, the transport network is divided into sub-networks of raw materials and products respectively. The upper-level model is assumed to make the decision about the location and the originäóñdestination (OD) matrices of raw materials and products flow. The lower-level is used to calculate the flow of each section under given OD flow matrices, and gives feedback to the upper model. An efficient heuristic method is introduced to solve this bi-level model. This bi-level model of construction industrial yard can help planners make a more scientific decision when establishing a new yard and promote the development of construction industrialization.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".