Parameters Identification of Moving Load Using ANN and Dynamic Strain

Joint Authors

Yang, Hui
Yan, Weiming
He, Haoxiang

Source

Shock and Vibration

Issue

Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-03-22

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Civil Engineering

Abstract EN

Moving load identification is an important part of bridge structure health monitoring; accurate and reliable load data can be used to check the load of bridge design, and the load spectrum can provide a more practical basis for structural fatigue analysis.

The method of the BP neural network is used in bridge moving loads identification.

The numerical examples of identification of the axle loads of a two-axle vehicle moving on a simply supported bridge under various speeds and weights are carried out.

The sensitivity of the bridge deflection and strain to moving loads is analyzed, and the influences of different activation function combinations and algorithm on network are discussed.

The identification results of different load conditions are analyzed and the effect of noise is considered.

Finally the rationality of the method is verified by experiments.

It is shown that the indirect estimation of vehicle weight by BP neural network from dynamic responses is feasible.

American Psychological Association (APA)

Yang, Hui& Yan, Weiming& He, Haoxiang. 2016. Parameters Identification of Moving Load Using ANN and Dynamic Strain. Shock and Vibration،Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119760

Modern Language Association (MLA)

Yang, Hui…[et al.]. Parameters Identification of Moving Load Using ANN and Dynamic Strain. Shock and Vibration No. 2016 (2016), pp.1-13.
https://search.emarefa.net/detail/BIM-1119760

American Medical Association (AMA)

Yang, Hui& Yan, Weiming& He, Haoxiang. Parameters Identification of Moving Load Using ANN and Dynamic Strain. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-13.
https://search.emarefa.net/detail/BIM-1119760

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1119760