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Record W4379374429 · doi:10.21838/uhpc.16706

Successes and Challenges of Two Experienced Canadian Architectural UHPC Precasters

2023· article· en· W4379374429 on OpenAlexaboutno aff
Gaston Doiron, Peter Seibert, Claudia Croteau, Trevor Harmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)ArchitectureArchitectural engineeringKey (lock)EngineeringArchitectural designComputer scienceConstruction engineeringVisual arts

Abstract

fetched live from OpenAlex

m3béton and Szolyd Concrete Corp. are two experienced architectural UHPC precasters who will share their insights on the challenges and successes of working with UHPC for over 10 years to create art installations, urban furniture and architectural elements. This presentation illustrates what allowed these innovative firms to become experts in the architectural UHPC field. In particular, unique key projects will be highlighted where many design and fabrication challenges needed to be resolved. Achieving excellent surface aesthetics while using extremely thin sections is often why UHPC is selected for these challenging projects. Surface finish, texture and colour are key elements for these elements and managing expectations is an integral part of the design. Fabrication of these intricate elements is only possible because the precasters have developed specific techniques and a great understanding of UHPC's properties and limits. This presentation will discuss the fundamentals of architectural UHPC and its technical challenges for surface finish, texture and complex shapes. The long-term performance of several completed projects by both architectural precasters after many years of use will be reviewed. The precasters' insights on the successes and challenges for these projects will be explained. Finally, what do these precasters see on the horizon for architectural UHPC?

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.983

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 head0.024
GPT teacher head0.232
Teacher spread0.209 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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