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Record W3134995589 · doi:10.2749/newyork.2019.0182

Robotics in Construction and the New Era of Efficient Concrete Bridges

2019· article· en· W3134995589 on OpenAlexaff
Paul Gauvreau

Bibliographic record

VenueReport · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBridge (graph theory)RobotStructural systemProduction (economics)RoboticsMechanizationComputer scienceReinforced concreteStructural approachConstruction engineeringEngineeringArtificial intelligenceCivil engineeringStructural engineeringEconomicsStructural changeMicroeconomics

Abstract

fetched live from OpenAlex

<p>Autonomous robots will most likely replace human labour as the primary means of production in bridge construction. This article examines the effect of this transformation of construction on the design of structural systems used for bridges. It begins with a review of changes made to structural systems in response to increases in construction wages in the 1950s and 1960s. High labour costs led to structural systems that were optimized to minimize the quantity of labour but which used materials inefficiently. The expected use of robots as the primary means of production in bridge construction is likely to have the opposite effect. Robots will lower the cost of production relative to human labour, thus making it worthwhile to design structural systems that use materials efficiently. Cast-in-place concrete holds good potential for use as the primary material in this new generation of efficient structural systems. Structural systems that proved themselves in the era of low construction wages prior to mechanization offer a solid basis for the development structural systems that take maximum advantage of the opportunities offered by robotic construction.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.371
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 teacher head, 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

Citations0
Published2019
Admission routes1
Has abstractyes

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