Uncertain inverse method by the sequential FOSM and its application on uncertainty reconstruction of vehicle–pedestrian collision accident

Cao, Liang; Liu, Jie; Xu, Can; Lu, Cheng; Xiaobing, Bu

doi:10.1007/s10999-020-09508-8

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…Now, the vertical displacement intervals of node 3 and node 7 are measured by experiments, which are δ3= [27.31, 36,17]mm, δ7=[54. 19,83.08]mm, and the loads F1 and F2 will be identified by the proposed uncertain inverse method. The forward model for laid identification is expressed as…”

Section: Examples and Discussionmentioning

confidence: 99%

“…Through the above discussion, it can be known that the IP-MRU is transformed into a small number of IP-RU under marginal CPs of variables U. In this paper, the uncertainty propagation-based inverse method [16,19] is adopted to realize the solving of IP-RU under each marginal CP.…”

Section: The Interval Inverse Methods Based On Hdmr and Affine Algorithm For Ip-rumentioning

confidence: 99%

“…Cao et al [19] proposed an effectively uncertain inverse method based the sequential first order and second moment and the maximum entropy principle. In the IP-MU, some system modeling parameters are uncertain, and the measured responses are deterministic.…”

Section: Introductionmentioning

confidence: 99%

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An efficient inverse method for structural identification considering modeling and response uncertainties

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et al. 2021

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The inverse problem analysis method provides an effective way for the structural parameter identification. Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters, the inverse of unknown structural parameter will face the challenges in the solving mechanism and the computational cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameter according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the 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 propagation 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 methods, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realize the inverse uncertainty quantification of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

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Section: Examples and Discussionmentioning

confidence: 99%

Section: The Interval Inverse Methods Based On Hdmr and Affine Algorithm For Ip-rumentioning

confidence: 99%

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An efficient inverse method for structural identification considering modeling and response uncertainties

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Liu

et al. 2021

Preprint

Self Cite

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Online adaptive selection of appropriate learning functions with parallel infilling strategy for Kriging-based reliability analysis

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Wu,

Zhu

et al. 2024

Computers & Industrial Engineering

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Uncertainty analysis of vehicle-pedestrian accident reconstruction based on unscented transformation

Zhou

et al. 2023

Forensic Science International

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Uncertain inverse method by the sequential FOSM and its application on uncertainty reconstruction of vehicle–pedestrian collision accident

Cited by 5 publications

References 45 publications

An efficient inverse method for structural identification considering modeling and response uncertainties

An efficient inverse method for structural identification considering modeling and response uncertainties

Online adaptive selection of appropriate learning functions with parallel infilling strategy for Kriging-based reliability analysis

Uncertainty analysis of vehicle-pedestrian accident reconstruction based on unscented transformation

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