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Record W2753369382 · doi:10.24904/footbridge2017.09287

The KuBAaI Footbridges In Bocholt / Germany – The Client's Wish to Use Low Maintenance Materials

2017· article· en· W2753369382 on OpenAlexaboutno aff
Katrin Baumann, Markus Gabler, Edwin Thie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCivil and Structural Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)PedestrianArchitectural engineeringQuarter (Canadian coin)EngineeringWork (physics)Transport engineeringCivil engineeringConstruction engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The small city of Bocholt owns a former industrial area of 25 ha which will be restructured to an urban and cultural district for people to work, live and explore. As part of the new urban development four footbridges with a span of up to 47 m have been designed to connect the two parts of this area which are separated by the river Aa. The bridges form the starting point for the future development and spaces for public events. The connecting bridges are not only an architectural statement, but also show the transition from the former industrial origin to the new cultural urban district. The design of the bridges was chosen because it combines the future and the past. Three of the four bridges are newly designed whereas one former railroad bridge will be refurbished for the use as a pedestrian bridge. This paper will focus on the client's requirements to build architectural icons which will drive the development of the new quarter but to also design structures with low maintenance effort and long durability. Especially smaller cities often lack a dedicated bridge department; they require therefore good guidance by the engineer and would typically prefer low-maintenance materials. This has been achieved by utilizing weathering steel as well as GFRP decks. Even though the initial costs as well as the public approval process for this innovative construction are higher compared to conventional materials, it will be cost-beneficial for the client through reduced maintenance costs in the long run.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0720.012

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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