Time-dependent fragility assessment of aged concrete gravity dam subjected to seismic load-with application to a dam in Korea
Bibliographic record
Abstract
Abstract\nDams play an essential role in society. Many concrete gravity dams in Korea and the world have been in service for some time and may already exceed their design working life. These dams are subjected to natural loads such as seismic loads. These old dams may need to be requalified by carrying out safety and reliability assessments. The reliability assessment is complicated by the fact that the strength and stiffness of the concrete are uncertain and time-varying. The uncertainty in both the material properties as well as in the loads needs to be considered in evaluating the reliability of existing dams using simple or sophisticated techniques reliability analysis techniques.\nAn overall framework to assess the fragility and safety of concrete gravity dam subjected to the seismic load is presented in the present thesis. The framework emphasizes the practical issues on the time-dependent seismic fragility curves assessment of gravity dam. The components of this framework consist of the nonlinear inelastic finite element modeling and dynamic analysis, the modeling of time-dependent concrete strength due to aging and degradation, the probabilistic analysis procedure leading to the fragility curves by considering failure criteria (i.e., limits state functions), and simple reliability analysis by considering seismic hazard.\nThe valuable and very limited number of samples from an actual dam is used to develop and validate the adopted time-dependent model of concrete strength. Nonlinear inelastic finite element models of an existing concrete gravity dam - Chungju Dam in Korea are developed and used to show the applicability of the proposed framework to assess the time-dependent seismic fragility curves and reliability. Two finite element software (one proprietary and the other commercially available software) are used to validate the developed finite element models. A sensitivity analysis of the dynamic characteristics of the dam to the material variability is presented by using the developed finite element models.\nFor the development of seismic fragility curves, several limit state functions based on cracking and displacements are considered, nonlinear inelastic time history analysis is performed, and the Latin hypercube sampling technique is employed for the probabilistic analysis. The results show the importance of considering the time-dependent concrete strength degradation in evaluating the time-dependent seismic fragility curves and reliability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".