Next Generation Hazard Resilient Infrastructure
Bibliographic record
Abstract
The resilience of underground infrastructure to large ground deformation depends on the ability of pipelines, cables, and conduits to accommodate the geometric nonlinearities in soil by changing shape through axial elongation/compression, flexure, and rotation at joints. This paper focuses on the development of the next generation hazard resilient infrastructure through large-scale testing and numerical modeling. With the assistance of the Cornell Lifelines Large-Scale Testing Facility, ten new pipeline and conduit systems have been developed and commercialized using a protocol of large-scale tests and fault rupture experiments that define and confirm performance under extreme conditions of ground deformation. Resilience involves the capacity of the pipelines to accommodate large ground deformation from earthquake-related movements associated with fault rupture, liquefaction, and landslides. It also involves the accommodation of ground movement caused by hurricanes, floods, tunneling, excavations, and subsidence related to mining and dewatering. The development and validation of analytical and numerical models for soil-structure interaction are described. The performance of the new systems is discussed. Examples of ductile iron, polyvinyl chloride, and steel pipelines, as well as those reinforced with cured-in-place pipe and pipe linings, are used to illustrate the performance of next generation hazard resilient infrastructure. Next steps in the development of hazard resilient infrastructure are discussed, which include the incorporation of smart sensor technologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".