Optimal Mix Ratios of Green Building Materials and Construction Cost Control
Bibliographic record
Abstract
Green concrete wall-building composite materials (namely GBMs, green building materials for short) have superior mechanical properties and good durability, thus they are now a high-profile topic in relevant research field. However, due to the existence of risk factors such as the high initial cost, and unclear policy direction, the promotion of GBMs has been greatly limited. In this context, it is a necessary work to study the cost control of construction projects, and figure out the relationship between the saving of building operating cost and the increase of initial construction cost. For this purpose, this study experimented on the optimal mix ratios of GBMs to control the construction budget of engineering projects. At first, this paper modeled the strain-hardening and steady-state cracking of GBMs, and constrained the models based on the bridging rule, the energy criterion, and the strength criterion. Then, this paper constructed a fractal model for the dense effect of powder particles in the aggregates of the GBMs, and analyzed its fractal features; after that, this paper designed the mix ratios of GBMs and experimented on the mechanical properties; at last, this paper constructed a production cost-use benefit model of the GBMs, completed the construction budget control of the engineering project, and gave experimental results of the performance of GBMs and the use cost calculation and analysis results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".