Accelerated Construction of Bridges: The Path Toward a Holistic Decision-Making System
Bibliographic record
Abstract
Approximately 28% of the 590,000 bridges in US need to be rehabilitated or replaced in the near future. Unless timely corrective action is taken, the social, environmental and economic costs associated with a declining infrastructure system are likely to be enormous. This mission is especially challenging because it must be achieved under heavy and escalating traffic conditions and using limited funds. The FHWA is promoting the philosophy of accelerated construction of bridges in order to mitigate the impact of reconstruction on the flow of traffic and to enhance quality of work and safety. Accelerated Construction has been performed in some high profile marquee projects, but it has not become the standard operating practice yet. Due to severe funding constraints, most state DOTs use initial cost as a primary factor in determining the technique for construction. This paper presents the background work done in a research study to develop a holistic decision making system based on a wide array of contributing factors. A comprehensive overview of the need for accelerated construction of bridges is presented. A review of decision making systems for bridge construction, found in research literature is presented and finally the results of a survey of state highway authorities in US and Canada detailing the state of the art of use of accelerated construction of bridges and factors considered while making decision are presented. The elements of a better decision making system such as cost, flow of traffic, safety, impact on local communities etc. are presented.
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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 | 0.046 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".