Comprehensive Fuzzy Assessment on the Life-Cycle Environment Impact of Bridges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A life cycle assessment (LCA) framework using fuzzy mathematics was developed to evaluate the environmental impact of bridges. The bridge life was divided into 5 stages: bridge design, raw materials processing, construction, operation, and demolition. An evaluation index system was established by analyzing the environmental impact of a bridge. Bridge life-cycle environmental impact was categorized into 5 grades: great negative influence, little negative influence, no influence, little positive influence, and great positive influence. Based on the improved AHP method, a fuzzy method was introduced to evaluate comprehensive environmental impact of bridges. The Highway Yangtze River Bridge in TaiZhou City in JiangSu Province was analyzed as a representative case study. Results show that major environmental impact appears during raw materials processing and construction. The method can be used to assess the life-cycle environmental impact of construction projects and help the stakeholders make decisions.
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.
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) | 0.002 | 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 it