Life-cycle cost assessment model for fiber reinforced polymer bridge deck panels
Bibliographic record
Abstract
To enhance the application of fiber reinforced polymer (FRP) bridge deck panels in the infrastructure area, a practical method is required that would allow probable assessment of the life-cycle cost of advanced composite applications in construction compared with that of conventional materials, at various discount rates, while integrating the available reference data. The overall objective of this research is to develop a performance-based probable life-cycle cost assessment model for FRP bridge deck panels. The life-cycle cost assessment model for FRP bridge deck panels comprises a life-cycle performance module (module-1) and a life-cycle cost optimization module (module-2). The model thus developed in this paper can then be used for other applications of composites in construction. The objective of module-1 is to develop an analytical model that is capable of predicting the structural deterioration over time to assess the deterioration rating per year of FRP bridge deck panels. The objective of module-2 is to develop an analytical model that is capable of assessing the optimal life-cycle cost of FRP bridge deck panels. Three case studies were conducted to validate the logic and results of the process algorithm for the life-cycle cost assessment. The model will be very helpful for the construction industry in evaluating various material options and to justify or deny the feasibility of using composite materials on specific construction projects. Since the life-cycle cost assessment of composite materials in construction has not been dealt with as proposed, it is anticipated that many of the procedures and systems mentioned would include fundamental research and possible innovations.Key words: fiber reinforced materials, Monte Carlo method, life-cycle cost, performance valuation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".