A Study on Asset Valuation Method for Bridge Asset management
Bibliographic record
Abstract
효율적인 교량의 유지관리를 위해서는 우선순위를 고려한 유지보수비용 예측과 전략적인 예산배분이 가능한 자산관리시스템의 구축이 필요하다. 본 연구는 이를 돕기 위한 교량의 자산가치 평가방법 연구를 통해 국내 실정에 적합한 실효성 있는 자산가치 평가방법의 제안을 주목적으로 하였다. 우선 국내외 교량시설물의 자산가치 평가 적용사례를 조사하여 그 장단점을 파악하고 국내 실정에 적합한 교량자산가치 평가 방법을 고찰하였다. 이를 바탕으로 취득원가에 의한 자산가치 평가방법과 대체원가를 활용한 가치평가 방법을 제안하고 교량의 가치평가를 위한 모델을 정립하였다. 또한 제안된 두 가지 자산가치 평가방법을 활용하여 국내에서 공용중인 교량의 가치평가를 수행하였다. 회계적 목적의 자산가치 평가 지원을 위해서는 초기건설비용에 근거한 취득원가를 고려한 자산가치 평가방법이 바람직한 것으로 분석되었으며, 유지관리 의사결정의 목적을 위해서는 보다 다양한 의사결정 인자의 고려가 가능한 감가상각 후 대체원가방법을 활용하는 것이 적합한 것으로 분석되었다. For efficient maintenance management of bridges, an establishment of asset management system is necessary which helps prediction of maintenance cost and strategic allocation of budget in consideration of top priority. The main purpose of this study is to suggest asset valuation method, which is practical in conformity with domestic situations, through researches on asset valuation method of bridges. This study has researched asset valuation method of bridge, which is appropriate for domestic situations by finding out advantages and disadvantages through investigating domestic and foreign application examples of asset valuation method for bridge facilities. In this study, asset valuation method by historical cost and replacement cost were suggested and a valuation model for bridges was established. In addition, two suggested valuation methods were applied to actual bridges which is used in Korea. As the result, it was analyzed that bridge asset valuation method in consideration of historical cost is desirable for the accounting purpose. And, it was analyzed that valuation method utilizing depreciated replacement cost(DRC), which could consider various factors, is desirable for the maintenance decision supporting purpose.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".