Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

Enokida, Ryuta; Kajiwara, Koichi

doi:10.1002/stc.2606

Cited by 6 publications

(14 citation statements)

References 45 publications

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“…A series of shake table substructuring experiments was performed on the basis of the SDOF specimen, which was used in the shake table experiments using NSBC. 33,49 Thus, the dynamics of the table sustaining this specimen are described by Equation ( 29) and the pure time delay (τ = 4.0 ms). The numerical substructure has the identical parameters used in the numerical example.…”

Section: Experimental Conditionsmentioning

confidence: 99%

“…Detailed parameters for the numerical simulations were determined from shake table experiments performed for examining NSBC. 33,49 This NSBC experiment had an SDOF specimen on a single-axis shake table. The weight of the shake table, including the experimental rigs, was approximately m ps = 250 kg, whereas that of the SDOF specimen was 200 kg.…”

Section: Detailed Structural Parametersmentioning

confidence: 99%

“…Detailed parameters for the numerical simulations were determined from shake table experiments performed for examining NSBC 33,49 . This NSBC experiment had an SDOF specimen on a single‐axis shake table.…”

Section: Numerical Examination Of Nlscmentioning

confidence: 99%

“…The weight of the shake table, including the experimental rigs, was approximately m ps = 250 kg, whereas that of the SDOF specimen was 200 kg. According to a system identification experiment, 33,49 the shake table was found to have a pure time delay of τ = 4.0 ms and the following dynamics:

({, \begin{array}{l} G_{d} ((), s) goodbreak= \frac{y_{ps} ((), s)}{u ((), s)} goodbreak= \frac{276.3 s^{2} + 165.8 s + 5.941 e 04}{s^{4} + 17.67 s^{3} + 673.3 s^{2} + 3732 s + 5.941 e 04} \\ G_{a} ((), s) goodbreak= \frac{{\ddot{y}}_{ps} ((), s)}{u ((), s)} goodbreak= s^{2} G_{d} ((), s) \end{array}) .

…”

Section: Numerical Examination Of Nlscmentioning

confidence: 99%

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Nonlinear substructuring control for simultaneous control of acceleration and displacement in shake table substructuring experiments

Enokida

2021

Structural Contr & Hlth

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This paper reports the first application of nonlinear substructuring control (NLSC) to shake table experiments in which the table dynamics is significantly affected by the specimen. Substructuring experiments using a shake table require simultaneous control of acceleration and displacement of the table because it functions as both an actuation system and a part of the substructure in the experiments. NLSC was developed as an enhanced version of linear substructuring control (LSC), the basic method for the control of dynamically substructured system. This paper first describes the formulation of substructures for shake table experiments using a multiple-degree-of-freedom structure and NLSC design for the substructures. The stability analysis of NLSC, which has three controllers, becomes much more complicated, more frequently showing conditional stability than is observed in conventional experiments. To assess various conditionally stable systems, this paper proposes an equivalent approach that can systematically count encirclements of the critical point in Nyquist plots. In the stability analysis using the proposed approach, NLSC is more robust against parameter variations in the substructures than LSC. In addition, the error feedback controller in both LSC and NLSC enhances the robustness against the variations. This study numerically and experimentally examined control performances for shake table substructuring experiments involving nonlinear characteristics. In the examinations, NLSC achieved the expected displacement and acceleration control with stability and reasonable accuracy, whereas LSC could not. The error feedback controller design of NLSC was found to be essential for performing shake table substructuring experiments with severe nonlinear characteristics.

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Section: Experimental Conditionsmentioning

confidence: 99%

Section: Detailed Structural Parametersmentioning

confidence: 99%

Section: Numerical Examination Of Nlscmentioning

confidence: 99%

({, \begin{array}{l} G_{d} ((), s) goodbreak= \frac{y_{ps} ((), s)}{u ((), s)} goodbreak= \frac{276.3 s^{2} + 165.8 s + 5.941 e 04}{s^{4} + 17.67 s^{3} + 673.3 s^{2} + 3732 s + 5.941 e 04} \\ G_{a} ((), s) goodbreak= \frac{{\ddot{y}}_{ps} ((), s)}{u ((), s)} goodbreak= s^{2} G_{d} ((), s) \end{array}) .

…”

Section: Numerical Examination Of Nlscmentioning

confidence: 99%

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Nonlinear substructuring control for simultaneous control of acceleration and displacement in shake table substructuring experiments

Enokida

2021

Structural Contr & Hlth

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“…To estimate time-varying parameters in nonlinear structures, we recently developed a simple piecewise linearisation in time series (SPLiTS) to functionalise TDI for the estimation (Enokida and Kajiwara 2020). SPLiTS, linearises a nonlinear structure piecewise based on the displacement data in which its central point shift component (e.g., residual deformation in a seismically damaged structure) is intentionally minimised.…”

Section: Introductionmentioning

confidence: 99%

An acceleration-oriented form of simple piecewise linearisation in time series to assess seismically damaged structures

Enokida

Kajiwara

2023

Front. Built Environ.

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This study presents an acceleration-oriented form of simple piecewise linearisation in time series (SPLiTS) to assess the condition of a seismically damaged structure using only its measured acceleration. Its original form could estimate the physical parameters of nonlinear structures in the time domain using inversions of the displacement and acceleration, based on its piecewise linearisation. However, its reliance on measured displacement limited its application only to structures in heavily monitored environments, such as laboratories. To enhance its feasibility for structures with fewer sensors or improper displacement measurement cases, an acceleration-oriented form is introduced, which does not require displacement measurements. To maintain the procedure’s simplicity, the new form retains the basic signal processing techniques: integrations of acceleration and a multi-pass moving-average filtering technique, to obtain the displacement and velocity responses used in the inversion. Based on the principle of SPLiTS, which minimises the central-point shift components, the average filtering technique removes the distortion generated during integration. The new form was examined by applying it to E-Defense shake table experiments on a three-storey steel structure, which contains an improper displacement measurement case. Although the original and new forms reasonably estimated the physical parameters in proper measurement cases, only the new form was effective in the improper case. The examinations confirmed the effectiveness of the acceleration-oriented form relying on the basic techniques and its applicability to estimating physical parameters of the seismically damaged structure for its condition assessment.

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Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

Nabeshima

2023

Earthq Engng Struct Dyn

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The physical parameter identification of the dynamic mechanical model, such as stiffness identification, provides some valuable information for detecting post‐earthquake damage to building structures. During an earthquake, tracking changes in stiffness on the skeleton curve can be effective. It would also be helpful as an approximate evaluation if the method could be applied to structures with arbitrary poly‐linear hysteresis characteristics. However, previous research has not identified any poly‐linear hysteresis characteristics in a unified formulation. This paper proposes a new stiffness identification method in the time domain for building structures with any poly‐linear hysteresis characteristics using sparse modeling. The validity of the proposed method was investigated through numerical simulations and a full‐scale shaking table test.

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Simple piecewise linearisation in time series for time‐domain inversion to estimate physical parameters of nonlinear structures

Cited by 6 publications

References 45 publications

Nonlinear substructuring control for simultaneous control of acceleration and displacement in shake table substructuring experiments

Nonlinear substructuring control for simultaneous control of acceleration and displacement in shake table substructuring experiments

An acceleration-oriented form of simple piecewise linearisation in time series to assess seismically damaged structures

Damage detection based on stiffness identification in time domain for poly‐linear hysteresis using sparse modeling

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