Predictive models to estimate construction and life-cycle cost of conventional and prefabricated bridges during early design phases
Bibliographic record
Abstract
State DOTs often need to decide during the early design phase whether to build their bridges using conventional or prefabricated construction methods based on overall cost effectiveness. This often requires DOTs to develop reliable cost estimates of these alternative construction methods with only limited available data during the early design phase. To support DOTs in this challenging task, this paper presents the development of a practical decision support tool that integrates novel bridge cost estimating models. These models were developed using stepwise, LASSO, and best subset regression techniques and a dataset of 241 conventional and prefabricated bridges. The developed LASSO model outperformed the two other models, achieving a mean absolute percentage error of 12.3% for conventional and 14.4% for prefabricated bridge projects. The developed decision support tool and its cost estimating models are expected to support bridge planners in identifying the most cost-effective bridge construction method during the early design phase.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".