Delivering Safe, Cost-Effective, Sustainable Civil Infrastructure Projects under Conditions of Non-Stationarity
Bibliographic record
Abstract
This paper proposes, describes and diagrams a methodology for planning, designing, constructing, and operating civil infrastructure projects for an operating environment that is changing substantially and in ways that are not readily predictable. Its purpose is to offer a timely answer to the engineer’s follow-on question, “How do I deliver a safe, reliable and cost-effective infrastructure project in the face of these significant changes?” An extension of existing civil infrastructure project delivery practices, this methodology addresses these changing conditions by incorporating appropriate levels of robustness, resilience, redundancy, and adaptability into the project design. Global climate change, the result of the burning of fossil fuels, is altering significantly the statistical properties of the environmental design parameters engineers use in infrastructure design. Consequently, long-held design assumptions such as ambient temperatures, sea levels, storm intensity, and the likely extent of droughts and heat waves are no longer reliable. Unknowingly, today’s engineers are planning, designing, and constructing infrastructure projects that will not be able to cope with future operating conditions. Thus, it is critical that the engineering profession devise a way of delivering projects that accounts for these new and significantly changing environmental conditions. Adopting this or some modification to this methodology is essential if civil infrastructure projects are to function as specified, and be protective of public health, safety, and well-being.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".