Train-induced vibration prediction in multi-story buildings using support vector machine

Title:

Train-induced vibration prediction in multi-story buildings using support vector machine

Creator:

Yao, Jinbao, Yao, Baozhen, Du, Yuwei, and Jiang, Yonglei

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9b6d08da-cd7f-4173-842d-7791f34701c3
uuid:9b6d08da-cd7f-4173-842d-7791f34701c3
doi:10.14311/NNW.2014.24.005

Subject:

Vibration, railway, train, building, support vector machine, and shuffled frog-leaping algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Train-induced vibration prediction in multi-story buildings can effectively provide the effect of vibrations on buildings. With the results of prediction, the corresponding measures can be used to reduce the influence of the vibrations. To accurately predict the vibrations induced by train in multi-story buildings, support vector machine (SVM) is used in this paper. Since the parameters in SVM are very vital for the prediction accuracy, shuffled frog-leaping algorithm (SFLA) is used to optimize the parameters for SVM. The proposed model is evaluated with the data from field experiments. The results show SFLA can effectively provide better parameter values for SVM and the SVM models outperform a better performance than artificial neural network (ANN) for train-induced vibration prediction

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:1

Harvested from:

CDK

Metadata only:

false