MétaCan
Menu
Back to cohort
Record W2903298682 · doi:10.1145/3272127.3275085

Set-in-stone

2018· article· en· W2903298682 on OpenAlexaff
Christian Schumacher, Jonas Zehnder, Moritz Bächer

Bibliographic record

VenueACM Transactions on Graphics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSet (abstract data type)Computer scienceTopology optimizationScale (ratio)Mathematical optimizationStructural engineeringUltimate tensile strengthMathematicsFinite element methodMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

Large-scale binder jetting provides a promising alternative to manual sculpting of sandstone. The weak build material, however, severely limits its use in architectural ornamentation. We propose a structural optimization that jointly optimizes an ornament's strength-to-weight ratio and balance under self-weight, thermal, wind, and live loads. To account for the difference in the tensile and compressive strength of the build material, we turn the Bresler-Pister criterion into a failure potential, measuring the distance to failure. Integrated into an XFEM-based level set formulation, we minimize this potential by changing the topology and shape of the internal structure. To deal with uncertainties in the location of live loads, and the direction of wind loads, we first estimate loads that lead to the weakest structure, then minimize the potential of failure under identified worst-case loads. With the help of first-order optimality constraints, we unify our worst-case load estimation and structural optimization into a continuous optimization. We demonstrate applications in art, furniture design, and architectural ornamentation with three large-scale 3D printed examples.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score0.999

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)0.0020.001

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.

Opus teacher head0.039
GPT teacher head0.254
Teacher spread0.215 · 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.

Study designObservational
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

Citations36
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueACM Transactions on GraphicsSame topic3D Surveying and Cultural HeritageFrench-language works237,207