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Record W4392138600 · doi:10.51988/jtsc.v5i1.187

Analisis Kebutuhan Tulangan Kolom dan Balok pada Tribun Penonton Stadion Mini Pancing Provinsi Sumatera Utara

2024· article· id· W4392138600 on OpenAlexaff
Resolina Ailing, Muhammad Qarinur

Bibliographic record

VenueJURNAL TEKNIK SIPIL CENDEKIA (JTSC) · 2024
Typearticle
Languageid
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPhysicsHumanitiesArt

Abstract

fetched live from OpenAlex


 Analisa kebutuhan tulangan untuk pekerjaan kolom dan balok penting dilaksanakan untuk mengantisipasi kekurangan dan kelebihan tulangan yang akan direncanakan. Pada penelitian ini, analisis kebutuhan tulangan dilakukan pada Stadion Mini Pancing Provinsi Sumatera Utara. Dalam mengoptimalkan penggunaan besi tulangan digunakan suatu metode, salah satunya metode yang sering digunakan yaitu Bar Bending Schedule (BBS). BBS adalah suatu metode yang memuat daftar model pola-pola pemotongan besi tulangan yang berfungsi untuk mengontrol pemakaian besi tulangan dan meminimalkan sisa material dengan bantuan aplikasi Microsoft Excel. Hasil dari analisis volume kebutuhan tulangan pada kolom dan balok menggunakan sambungan konvensional adalah 54.830,96 kg, sedangkan hasil dari analisis volume kebutuhan tulangan pada kolom dan balok menggunakan sambungan kopler adalah 53.523,57 kg. Hasil perbandingan volume kebutuhan tulangan pada kolom dan balok menggunakan sambungan konvensional dengan sambungan kopler adalah 2,3 %. Hasil perhitungan kebutuhan biaya penulangan kolom dan balok menggunakan sambungan konvensional adalah Rp743.507.817,60 sedangkan sambungan kopler adalah Rp725.688.195,00. Oleh karena itu dapat disimpulkan sambungan kopler lebih efisien untuk digunakan dibandingkan sambungan konvensional dari segi biaya.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.058
GPT teacher head0.407
Teacher spread0.350 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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