The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in the practical engineering inverse problems. Due to the coupling of multi-source uncer- tainties in the measured responses and the modeling parameters, the traditional inverse method under the determin-istic framework faces the challenges in solving mechanism and computing cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identifi-cation of unknown structural parameters according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters. Afterwards, a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty. The transformed interval inverse problem involves the two-layer solving process including interval propa-gation and optimization updating. In order to solve the interval inverse problems considering response uncertainty, an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed. Through the coupling of the above two strategies, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realizes the uncertainty identifica-tion of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

Lixiong Cao;Jie Liu;Cheng Lu;Wei Wang

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body,College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410082,China

中国机械工程学报

[1]Lixiong Cao;Jie Liu;Cheng Lu;Wei Wang-.Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties)[J].中国机械工程学报,2022(05):118-129

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