Fishbone model-based inversion to estimate physical parameters of elastic structures under earthquake excitations
Author(s): |
Koichi Kajiwara
Akiko Kishida Jun Fujiwara Ryuta Enokida |
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Medium: | journal article |
Language(s): | English |
Published in: | Frontiers in Built Environment, February 2023, v. 9 |
DOI: | 10.3389/fbuil.2023.1201048 |
Abstract: |
This study established an inversion based on a fishbone model to estimate physical parameters from the responses of elastic building structures subjected to an earthquake. A fishbone model, which has rotational springs and dashpots in addition to the elements in a lumped mass model, is effective for demonstrating structural rotations that happen at the connections of columns and beams. This model is commonly applied to computational calculations of seismic responses of structures and is classified into a forward problem obtaining responses from known systems and excitations. Although its effectiveness for the forward problem has been well demonstrated, it has rarely been applied to the inverse problem, where structural properties are estimated from known responses and excitations. First, this study inverted multi/single-mass-system fishbone models. Then, the inversion was applied to an elastic fishbone model of a 3-mass system, which was built based on an E-Defense shaking table experiment, and its structural responses were numerically simulated. This numerical simulation demonstrated its effectiveness for accurately estimating parameters in the fishbone model of the 3-mass system, especially when its structural responses are not contaminated by noises. Lastly, it was applied to responses containing some noise to examine its influence on the estimation accuracy. The estimation accuracy of damping elements was found to be sensitive to noise, whereas that of stiffness was more insensitive than the damping elements. The proposed inversion is particularly suitable for estimating rotational stiffness, which is not obtainable from the inversion of lumped mass systems. |
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data sheet - Reference-ID
10739896 - Published on:
02/09/2023 - Last updated on:
02/09/2023