Probabilistic Nonlinear Displacement Ratio Prediction of Self-centering Energy-absorbing Dual Rocking Core System under Near-fault Ground Motions Using Machine Learning
Bibliographic record
Abstract
Near-fault pulse-like ground motions can lead to significant seismic demand on building structures due to velocity pulses. The self-centering energy-absorbing dual rocking core (SEDRC) system is a newly developed seismic resilient structural system. This paper investigates the seismic demand of SEDRC systems subjected to near-fault pulse-like ground motions by determining their nonlinear displacement ratios. Two hundred and five near-fault pulse-like ground motion records are used to consider the uncertainties of seismic events. The influences of design hysteretic parameters and near-fault ground motion characteristics on the nonlinear displacement ratio of the SEDRC system are investigated through parametric dynamic analysis of single-degree-of-freedom (SDOF) systems. The dynamic analyses results indicate that the stiffness hardening ratio α and energy-absorbing ratio β of the SEDRC system, predominant period, pulse period of ground motions, and earthquake magnitude show obvious effects on the nonlinear displacement ratio responses of SDOF systems, while the unloading stiffness ratio ε, site condition, and source-to-site distance show limited or negligible influence on that. The past studies mainly used the mean or median responses of single-degree-of-freedom systems to predict the nonlinear displacement ratio of structures, which may not be enough to guide the design of buildings with great importance (e.g., hospital and fire station). This paper proposes an innovative framework for predicting the nonlinear displacement ratio of structures underground motions using a probabilistic estimation method and machine learning technique. Based on the dynamic analysis results of SDOF systems, a probabilistic model for predicting the nonlinear displacement ratio of the SEDRC system under near-fault pulse-like ground motions is developed through the proposed framework. The proposed framework is also applicable to estimate the seismic demand of other structures under near-fault or far-field ground motions to facilitate the development of the performance-based seismic design.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".