Life-Cycle Cost Adjustment Factors in Alternate Design/Alternative Bid Pavement Bids: Added Value or Added Controversy?
Bibliographic record
Abstract
Alternative design/alternative bids (ADAB) provides a mechanism for the asphalt and concrete paving industries to compete for the same paving project. It operates on the principle of the market pricing of each material determining which is most economical when the bids are opened, rather than selecting the pavement type during design based on a life-cycle cost analysis (LCCA). This paper reviews including LCC-based bid adjustment factors in the ADAB award decision. Data are from a survey that received responses from 40 U.S. Departments of Transportation (DOT) and the Canadian province of Ontario, and a content analysis of 55 ADAB project outcomes in 13 U.S. states and three Canadian provinces. Seven algorithms in use to calculate an ADAB bid adjustment factor were found, and six U.S. DOTs that award ADAB projects without an adjustment factor. The paper finds that the adjustment factor formula rarely influences the award decision and, generally, the pavement type with the lowest bid cost wins with or without the adjustment factor. The paper models the ADAB process in financial terms as an exercisable commodity option that accrues value from the differential rates of volatility between asphalt and concrete. It concludes that an LCC-based bid adjustment factor complicates the award process, creating potential for controversy over what the factor inputs are, and does not add value over bidding the pavement types head to head and awarding to the low bidder. The ADAB process increases the number of bidders and reduces unit bid prices for both pavement types.
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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 | 0.027 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".