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RSS FeedsAlgorithms, Vol. 10, Pages 62: Influence Factors Analysis on the Modal Characteristics of Irregularly-Shaped Bridges Based on a Free-Interface Mode Synthesis Algorithm (Algorithms)

 
 

28 may 2017 09:45:40

 
Algorithms, Vol. 10, Pages 62: Influence Factors Analysis on the Modal Characteristics of Irregularly-Shaped Bridges Based on a Free-Interface Mode Synthesis Algorithm (Algorithms)
 


In order to relieve traffic congestion, irregularly-shaped bridges have been widely used in urban overpasses. However, the analysis on modal characteristics of irregularly-shaped bridges is not exhaustive, and the effect of design parameters on modal characteristics will be deeply investigated in future studies. In this paper, a novel strategy based on a free-interface mode synthesis algorithm is proposed to evaluate the parameters` effect on the modal characteristics of irregularly-shaped bridges. First, a complicated, irregularly-shaped bridge is divided into several substructures based on its properties. Then, the modal characteristics of the overall structure can be obtained, only by a few low-order modal parameters of each substructure, using a free-interface mode synthesis method. A numerical model of a typical irregularly-shaped bridge is employed to verify the effectiveness of the proposed strategy. Simulation results reveal that the free-interface mode synthesis method possesses favorable calculation accuracy for analyzing the modal characteristics of irregularly-shaped bridges. The effect of design parameters such as ramp curve radius, diaphragm beam stiffness, cross-section feature, and bearing conditions on the modal characteristics of an irregularly-shaped bridge is evaluated in detail. Analysis results can provide references for further research into and the design of irregularly-shaped bridges.


 
97 viewsCategory: Informatics
 
Algorithms, Vol. 10, Pages 61: Design and Implementation of a Multi-Modal Biometric System for Company Access Control (Algorithms)
Algorithms, Vol. 10, Pages 63: Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks (Algorithms)
 
 
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