An Unconstrained Structural Dynamic Load Reconstruction Method Based on the Sparse Bayesian Learning Algorithm

CHEN Xianzhi; ZHOU Xinyuan; ZENG Yaoxiang; ZHANG Yahui

doi:10.21656/1000-0887.430336

Volume 44 Issue 8

Aug. 2023

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Article Navigation > Applied Mathematics and Mechanics > 2023 > 44(8): 931-943

CHEN Xianzhi, ZHOU Xinyuan, ZENG Yaoxiang, ZHANG Yahui. An Unconstrained Structural Dynamic Load Reconstruction Method Based on the Sparse Bayesian Learning Algorithm[J]. Applied Mathematics and Mechanics, 2023, 44(8): 931-943. doi: 10.21656/1000-0887.430336

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An Unconstrained Structural Dynamic Load Reconstruction Method Based on the Sparse Bayesian Learning Algorithm

doi: 10.21656/1000-0887.430336

1.
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China
2.
Beijing Institute of Astronautical System Engineering, Beijing 100076, P.R.China

Received Date: 2022-10-24
Rev Recd Date: 2022-11-15
Publish Date: 2023-08-01

Abstract

Abstract

For rapid and exact reconstruction of dynamic loads on unconstrained structures with unknown initial conditions, a dynamic load reconstruction method was proposed based on the sparse Bayesian learning algorithm. With the idea of the function fitting technique, the control equations were built. The noise was assumed to obey the Gaussian distribution, and the fast algorithm was used in the sparse Bayesian learning model. An improved piecewise fitting method was formulated to rationally express the initial conditions in the piecewise fitting, the end state response of the previous segment was used as the possible initial condition, and the low-order vibration modes were applied as the supplement to the initial displacements and initial velocities. The numerical simulations of simplified launch vehicle models prove the accuracy and efficiency of the proposed method, under the effects of different noise levels and different expressions of initial conditions.
- function fitting,
- sparse Bayesian learning algorithm,
- modified piecewise fitting,
- load reconstruction

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References(24)

References

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