Analisis Tingkat Akurasi Uji Pemadatan dengan Pendekatan Numerik Berbasis Elemen Hingga
Bibliographic record
Abstract
ABSTRAK Penelitian ini membahas tentang simulasi model pemadatan antara uji laboratorium dibandingkan lapangan menggunakan PLAXIS 2D 2017 berbasis elemen hingga. Simulasi model kedua pengujian tersebut dilakukan dengan variasi beban energi yang sama untuk membandingkan perbedaan hasilnya. Simulasi model pemadatan di lapangan dilakukan dengan variasi beban, siklus dan tebal lapis pemadatan, sedangkan di laboratorium hanya variasi beban energi dan tebal lapis pemadatan berdasarkan berat isi kering. Hasil uji pemadatan berat isi kering di laboratorium sebesar 1,546 kN/m2, sedangkan hasil simulasi model sebesar 1,6 kN/m2, sehingga tingkat akurasi perbedaannya sebesar 3,49%. Simulasi model pemadatan lapangan dengan variasi beban pada tebal lapis pemadatan yang sama menunjukan bahwa semakin besar beban pemadatan akan menghasilkan jumlah lintasan (siklus) lebih sedikit, sehingga dapat disimpulkan bahwa semakin tebal lapis pemadatan nilai penurunannya akan semakin besar. Kata Kunci: simulasi model analisis pemadatan, pemadatan laboratorium dan lapangan, tebal lapis pemadatan, nilai penurunan. ABSTRACT This research studied compaction modelling between laboratory simulation test and field test using PLAXIS 2D 2017 based on finite element method. Each tests was carried out with the same energy variations to compare the discrepancy of the result. A model with the variation in load energy, cycle and thickness of compaction layer was made to simulate the field test, while for the laboratory test, only variation in load energy and thickness of compaction layer is used based on dry unit weight. The results of the laboratory compaction test of dry unit weight was 1.546 kN/m2, while the model simulation were 1.6 kN/m2. So the accuracy difference between both is 3.49%. Simulation of field model with variations in load at the same thickness of compaction layer shows that the greater load of compaction will produced less number of cycles. It can be concluded that the thicker compaction layer will increased settlement value. Keywords: compaction model analysis, field and laboratory compaction, thickness compaction and settlement.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".