Perbandingan Metode Penskalaan Amplitudo dan Pencocokan Spektral terhadap Respons Non-Linear Jembatan Terisolasi dengan LRB: Studi Dampak pada Perilaku Histeresis dan Potensi Akumulasi Kerusakan LRB
Bibliographic record
Abstract
Abstrak Analisis Riwayat Waktu Non-Linear (NLTHA) merupakan metode yang efektif untuk merepresentasikan respons struktural akibat beban gempa secara akurat. Dalam analisis NLTHA, pemilihan metode modifikasi gerak tanah memiliki peran yang krusial, dimana secara umum terdapat dua metode yaitu penskalaan amplitudo dan pencocokan spektral. Data gerak tanah yang akan di modifikasi didapatkan berdasarkan hasil analisis deagregasi untuk wilayah Jakarta dengan periode ulang 1000 tahun. Penelitian ini bertujuan untuk membandingkan dampak pemilihan kedua metode modifikasi gerak tanah terhadap potensi kerusakan elemen non-linear berupa Lead Rubber Bearing (LRB) pada struktur berupa jembatan terisolasi seismik. Untuk tujuan ini, tujuh pasang rekaman gerak tanah diolah dengan penskalaan amplitudo yang mengacu pada standar NZS 1170.5-2004, sedangkan pencocokan spektral dilakukan menggunakan perangkat lunak SeismoMatch. Intensitas Arias (IA) merepresentasikan total energi input gempa yang terserap ke dalam sistem struktur, sehingga dari kedua metode akan dilakukan pengecekan terhadap IA. Dari hasil penelitian yang telah dilakukan, menunjukkan bahwa metode pencocokan spektral mencapai kecocokan yang superior terhadap target spektrum. Namun, metode ini mengubah profil akumulasi energi gempa rekaman gerak tanah asli, mengindikasikan terjadinya modifikasi distribusi energi input. Sebaliknya, penskalaan amplitudo berhasil mempertahankan IA yang identik dengan rekaman gerak tanah asli. IA berkorelasi kuat dengan potensi kerusakan kumulatif dan disipasi energi histeresis, sehingga penskalaan amplitudo dinilai lebih relevan untuk memprediksi akumulasi kerusakan LRB. Kata-kata Kunci: Jembatan Terisolasi, LRB, Pencocokan Spektral, Penskalaan Amplitudo, Perilaku Histeresis, Intensitas Arias, Respons Non-Linear. Abstract Nonlinear Time History Analysis (NLTHA) is an effective method for accurately representing structural response under seismic loading. In NLTHA, the selection of ground motion modification methods plays a crucial role, with two primary approaches commonly employed: amplitude scaling and spectral matching. The ground motion data to be modified were obtained based on deaggregation analysis for the Jakarta region with a 1000-year return period. This study aims to compare the impact of selecting these two ground motion modification methods on the potential damage of nonlinear elements, specifically Lead Rubber Bearing (LRB), in seismically isolated bridge structures. For this purpose, seven pairs of ground motion records were processed using amplitude scaling following the NZS 1170.5-2004 standard, while spectral matching was performed using SeismoMatch software. Arias Intensity (IA) represents the total seismic input energy absorbed into the structural system; therefore, IA was evaluated for both modification methods. The research results demonstrate that the spectral matching method achieves superior agreement with the target spectrum. However, this method alters the energy accumulation profile of the original ground motion records, indicating modification of the input energy distribution. Conversely, amplitude scaling successfully maintains IA identical to that of the original ground motion records. IA strongly correlates with cumulative damage potential and hysteretic energy dissipation; therefore, amplitude scaling is considered more relevant for predicting LRB damage accumulation. Keywords : Spectral Matching, Amplitude Scaling, Hysteretic Behavior, Arias Intensity, Non-Linear Response
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".