期刊
ADVANCES IN STRUCTURAL ENGINEERING
卷 24, 期 11, 页码 2336-2350出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1369433221997722
关键词
bridge groups; machine learning; medium- and small-span girder bridges; statistical distributions; vibration characteristic analyses
资金
- Science and Technology Project of Transportation Group of Shanxi Province [18-JKKJ-08, 19-JKKJ-11]
- Transportation Construction Science and Technology Project of Department of Transportation of Shanxi Province [2020-2-01]
- Transportation Construction Science and Technology Project of the Transportation Department of Inner Mongolia Autonomous Region [NJ-2020-17]
A framework based on machine learning models was proposed to analyze the vibration characteristics of specific line bridge groups in this study. Random forest models and artificial neural network models showed good performance in predicting vibration modes and periods, providing an intelligent and efficient method for obtaining vibration characteristics of bridge groups for a specific network.
There are large amounts of small-and medium-span girder bridges which bear structural similarity, while the large-scale bridge structures are generally limited in the timely applications of structural vibration characteristics. Therefore, in this study a framework based on machine learning models was proposed to analyze the vibration characteristics of specific line bridge groups. The probability distributions of structural, geometric, and material properties of bridge groups in specific lines were obtained using statistical tools and a Latin hypercube sampling method was used to generate reasonable sample sets for the bridges group, and parameterized finite element models of the bridges were established. Then, the optimal models were tuned and determined to predict fundamental mode and period by the 10-fold cross-validation method applying the numerical simulation results. This study's results showed that the random forest models divided the vibration modes of the bridge groups into the longitudinal vibrations of the main girders and the longitudinal vibrations of the adjacent spans and side piers with a classification accuracy of greater than 90%, while the artificial neural network models exhibited the lowest normalized mean square error for the periods. The periods mainly ranged between 0.7 and 1.5 s. Furthermore, the bearing settings, ratios of the pier height to section diameters, and boundary types were determined to be the most significant properties influencing the fundamental modes and periods of the examined bridges, by respectively observing the reduced value of the random forest Gini indices and distribution of the generalized weight value of the input variables in artificial neural networks. This study provides an intelligent and efficient method for obtaining vibration characteristics of bridges group for a specific network.
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