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Record W3179815903 · doi:10.3390/civileng2030030

Discrete Rigid Block Analysis to Assess Settlement Induced Damage in Unreinforced Masonry Façades

2021· article· en· W3179815903 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCivilEng · 2021
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsCarleton University
FundersUniversity of Nebraska-Lincoln
KeywordsMasonryUnreinforced masonry buildingDiscrete element methodStructural engineeringSettlement (finance)Computer scienceBlock (permutation group theory)GeologyEngineeringMathematicsMechanicsGeometryPhysics

Abstract

fetched live from OpenAlex

In this study, a system of discontinuous rigid blocks is employed to simulate the possible damage mechanisms in unreinforced masonry (URM) façades and load-bearing frame systems subjected to settlement using the discrete element method (DEM). First, the employed modeling strategy is validated utilizing the available experimental results presented in the literature. Once there is a good agreement between the computational models and experimental findings, a sensitivity analysis is performed to quantify the influence of the input parameters defined in the DEM-based numerical model. Finally, the proposed modeling strategy is further utilized to assess the damage pattern that may develop in a URM façade due to uniform and non-uniform settlement profiles. The results of this study clearly show that the discrete rigid block analysis (D-RBA) provides robust numerical solutions that can be employed to visualize and assess the possible damage patterns and related collapse mechanisms of URM masonry systems as an alternative modeling strategy to standard continuum-based solutions.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
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
Bibliometrics0.0000.002
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 head0.020
GPT teacher head0.253
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