Life-Cycle Cost Adjustment Factors in Alternate Design/Alternative Bid Pavement Bids: Added Value or Added Controversy?
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
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.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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