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Record W4307182195 · doi:10.15788/ndot2022.09

Improving Connectivity: Innovative Fiber-Reinforced Polymer Structures for Wildlife, Bicyclists, and/or Pedestrians

2022· report· en· W4307182195 on OpenAlexfundno aff
Matthew Bell, Rob Ament, Damon Fick, Marcel P. Huijser

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersOregon Department of TransportationNevada Department of TransportationArizona Department of TransportationNew Mexico Department of TransportationParks CanadaMinnesota Department of TransportationFederal Highway AdministrationMichigan Department of TransportationU.S. Department of Transportation
KeywordsWildlifeTransport engineeringBridge (graph theory)Wildlife corridorCivil engineeringEnvironmental scienceEngineeringEcology

Abstract

fetched live from OpenAlex

Engineers and ecologists continue to explore new methods and adapt existing techniques to improve highway mitigation measures that increase motorist safety and conserve wildlife species. Crossing structures, overpasses and underpasses, combined with fences, are some of the most highly effective mitigation measures employed around the world to reduce wildlife-vehicle collisions (WVCs) with large animals, increase motorist safety, and maintain habitat connectivity across transportation networks for many other types and sizes of wildlife. Published research on structural designs and materials for wildlife crossings is limited and suggests relatively little innovation has occurred. Wildlife crossing structures for large mammals are crucial for many highway mitigation strategies, so there is a need for new, resourceful, and innovative techniques to construct these structures. This report explored the promising application of fiber-reinforced polymers (FRPs) to a wildlife crossing using an overpass. The use of FRP composites has increased due to their high strength and light weight characteristics, long service life, and low maintenance costs. They are highly customizable in shape and geometry and the materials used (e.g., resins and fibers) in their manufacture. This project explored what is known about FRP bridge structures and what commercial materials are available in North America that can be adapted for use in a wildlife crossing using an overpass structure. A 12-mile section of US Highway 97 (US-97) in Siskiyou County, California was selected as the design location. Working with the California Department of Transportation (Caltrans) and California Department of Fish and Wildlife (CDFW), a site was selected for the FRP overpass design where it would help reduce WVCs and provide habitat connectivity. The benefits of a variety of FRP materials have been incorporated into the US-97 crossing design, including in the superstructure, concrete reinforcement, fencing, and light/sound barriers on the overpass. Working with Caltrans helped identify the challenges and limitations of using FRP materials for bridge construction in California. The design was used to evaluate the life cycle costs (LCCs) of using FRP materials for wildlife infrastructure compared to traditional materials (e.g., concrete, steel, and wood). The preliminary design of an FRP wildlife overpass at the US-97 site provides an example of a feasible, efficient, and constructible alternative to the use of conventional steel and concrete materials. The LCC analysis indicated the preliminary design using FRP materials could be more cost effective over a 100-year service life than ones using traditional materials.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.289
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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