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Record W2928370146 · doi:10.11159/icsect19.112

Suspension Hammering Tester for Concrete Walls

2019· article· en· W2928370146 on OpenAlexvenueno aff
Saeko TOKUOMI

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsSuspension (topology)Materials scienceComputer scienceComposite materialMathematics

Abstract

fetched live from OpenAlex

Inspection method of concrete structures is typically hammering test. In the hammering test workers tap surfaces of concrete with a hammer. However, workers' testing is not economical in both time and money. So we have developed a suspension hammering tester for concrete walls which includes an impact acoustic apparatus to detect defects. The tester ascends/descends by a winch mounted to the rooftop of buildings, ascending/descending at a speed of 0.5 m/s. The tester has four wheels, four impactors, two microphones, and four fans that apply pressure in order for the machine to adhere to concrete walls. Four impactors are positioned at equal intervals of 150mm on a horizontal axis combining to create a 0.5m wide testing range during a single ascent or descent. So, the testing speed is 0.5 m/s * 0.5 m = 0.25 m/s 2 . The dimensions of the machine are 70 cm wide, 80 cm long and 18 cm thick.

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 score0.464
Threshold uncertainty score0.931

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 head0.179
Teacher spread0.175 · 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
Published2019
Admission routes1
Has abstractyes

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