Bibliographic record
Abstract
The goal of this research is to develop a methodology to accurately determine the reliability of spillway gate systems particularly for spillways that experience harsh environmental conditions and prolonged periods of dormancy. The significance of this study lies in the fact that spillways are rarely in use and remain inactive for most of their service life. Components of emergency spillway gate systems spend the majority of their service life in a dormant state and are activated only during emergencies such as floods or load rejection or on a regular basis for inspection and testing. Also, most spillways are located in remote areas and are subjected to severe environmental conditions which can cause early degradation of components. Furthermore, components of old spillway gate systems are often custom made with no readily available spare parts and little information on the reliability of existing components. These characteristics are very different from those of the equipment used in an industrial setting making it difficult for traditional methods to deliver accurate estimates on the reliability of such systems. Therefore, the development of a methodology that is customized to such conditions and incorporates unique parameters and state-of-the-art reliability techniques can contribute greatly to the dam industry by ensuring the safe operation of spillway systems on demand. The first step in this approach is geared towards system modeling in which a reliability model is developed for the spillway gate system taking into account all components, their relative interactions, latent failures due to dormancy, environmental conditions and type and frequency of inspections and tests. The next step is to develop a quantitative approach to update the availability of the spillway gate system based on real time conditions after each inspection. In this step, a Condition Indexing (CI) approach is combined with dormant availability analysis to evaluate the changes in the state of the system in real time using CI data obtained at each inspection. This approach provides a tool for dam owners to convert qualitative and descriptive results obtained from inspections to an index used as a comparative measure to detect real time changes in the availability of spillway gate systems. Next, inspection and testing procedures of spillway gate systems are investigated to evaluate the effect of different types and frequencies on the reliability of various types of components and the entire system. Lastly, the optimum inspection and testing strategy is determined, minimizing system costs including costs related to inspection and testing and the consequences of failure while at the same time maintaining the availability of the spillway gate system above a predefined limit. Genetic algorithm and Creeping Random Search are used to solve this optimization problem. Using these methods the optimum interval for each type of test is determined and the minimum system cost is calculated based on the optimum intervals.This methodology is used to develop a software application that incorporates all of the above steps into a user friendly program. This software application has been developed for availability analysis of spillway systems and allows users to model complex systems, add inspection, tests and component replacement options to the system, determine the availability of the system as a function of service life and identify the optimum inspection and testing period based on unavailability limits and costs of inspections/tests vs. consequence of failure. This program can be used as a tool by dam owners to accurately determine the availability of custom spillways and to select optimal inspection and testing plans that contribute most to increase the availability of the system.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".