• Journal of the Korean Society for information Management
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• v.22 no.1 s.55
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• pp.191-208
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• 2005

This study utilizes various approaches for new topic detection in the process of assigning and updating descriptors, which is a representation method of the knowledge structure. Particularly in the case of occurring changes on the knowledge structure due to the appearance and development of new topics in specific study areas, new topic detection can be applied to solving the impossibility or limitation of the existing index terms in representing subject concepts. This study confirms that the majority of newly developing topics in information science are closely associated with each other and are simultaneously in the phase of growth and development. Also, this study shows the possibility that the use of candidate descriptor lists generated by new topic detection methods can be an effective tool in assisting indexers. In particular. the provision of candidate descriptor lists to help assignment of appropriate descriptors will contribute to the improvement of the effectiveness and accuracy of indexing.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.2
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• pp.115-122
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• 2016

The classical statistical approach using test data samples to estimate true value of Random Variables by calculating mean and variation (standard deviation or coefficient of variation) of samples is very useful to understand the existing condition of the structure. But with this classical approach, our prior knowledge through educational background and professional experience cannot provide any benefit to make decisions by the structural engineers. This paper shows the role of Bayesian methodology by providing chance of using valuable prior knowledge to come up with more accurate estimation of structural condition. This paper also shows how important it is to have a proper prior estimate of Random Variables and corresponding confidence level through gathering and studying more relevant information.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.