The Method for Determining Conditions to Vibrations Suppression of Bridge Beams.
Published In: International Journal of Structural Stability & Dynamics, 2024, v. 24, n. 12. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Poliakov, V.; Saurin, V.; Zhang, N. 3 of 3
Abstract
The paper deals with the problem of obtaining a given dynamic behavior of simply supported bridge beams from the impact of rolling stock. Resonant vibrations of beams become a reality at high speeds if the train is formed of identical cars, which is typical for passenger trains. The phenomenon of suppression of beam vibrations by a train, known from publications, is possible only with an exact ratio of the length of the car and the beam. This significantly reduces the possibilities of choosing the necessary spans of beams. Consideration of issues of interaction in the "bridge–track—train" system for security purposes requires the involvement of a rather complex mathematical apparatus and appropriate software. The paper proposes a new method for determining the specified dynamic behavior of bridge beams, suitable for any spans and available to engineers at the stage of pre-design assignment of dynamic parameters of beams. The requirements that are necessary and sufficient conditions for preventing the resonance of bridge beams on the HSR are determined. At the same time, it does not require the involvement of software based on a complex mathematical technique. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Structural Stability & Dynamics. 2024/06, Vol. 24, Issue 12, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2024
- ISSN:0219-4554
- DOI:10.1142/S0219455424501347
- Accession Number:178117043
- Copyright Statement:Copyright of International Journal of Structural Stability & Dynamics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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