Calculation Method For Asset Value Assessment of Municipal Transportation Infrastructure Based on Genetic Algorithm
Bibliographic record
Abstract
The necessity of national economic evaluation of highway construction projects under the current situation of rapid development of highway construction; This paper applies the asset value evaluation method to the evaluation of the residual value of highway assets in the national economic evaluation of highway, and discusses the method of using the replacement cost method in the asset value evaluation to determine the residual value of highway assets. Through the analysis of the influencing factors affecting the value of transportation infrastructure assets, the advantages and disadvantages of various evaluation methods are integrated, the most reasonable method is selected to evaluate the value of transportation infrastructure, and the technical status indicators of assets are scientifically and reasonably transformed into economic indicators. This paper introduces the estimation method of road pavement and Bridge assets and the estimated total capital and output. Genetic algorithm is an iterative adaptive probabilistic search algorithm based on the mechanism of natural selection and natural genetics in the biological world. As a new intelligent search algorithm, genetic algorithm has quickly attracted everyone’s attention after its birth, and has achieved good results in many fields. While calculating the mixture ratio, considering the price of raw materials, the multi-objective planning is truly achieved, which opens up a new direction for the calculation of mixture ratio in the whole highway construction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".