Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil

Kanellopoulos, Konstantinos; Psycharis, Nikolaos; Yang, Han; Jeremić, Boris; Anastasopoulos, Ioannis; Stojadinović, Božidar

doi:10.1016/j.soildyn.2022.107366

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Accordingly, the data-informed CNN method would consider the soil's nonlinear behavior during data generation for training as the DRM is capable of modeling nonlinearities. 46 This method can aid decision-makers in quickly assessing the structural integrity of infrastructure after an earthquake.…”

Section: Discussionmentioning

confidence: 99%

“…The presented method could also be extended to consider the nonlinearity of soils, given that near‐surface soils can exhibit nonlinear response behavior during moderate and strong earthquakes. Accordingly, the data‐informed CNN method would consider the soil's nonlinear behavior during data generation for training as the DRM is capable of modeling nonlinearities 46 . This method can aid decision‐makers in quickly assessing the structural integrity of infrastructure after an earthquake.…”

Section: Discussionmentioning

confidence: 99%

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Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Maharjan,

Guidio,

Jeong

2023

Earthq Engng Struct Dyn

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We introduce a novel data‐informed convolutional neural network (CNN) approach that utilizes sparse ground motion measurements to accurately identify effective seismic forces in a truncated two‐dimensional (2D) domain. Namely, this paper presents the first prototype of a CNN capable of inferring domain reduction method (DRM) forces, equivalent to incident waves, across all nodes in the DRM layer. It achieves this from sparse measurement data in a multidimensional setting, even in the presence of incoherent incident waves. The method is applied to shear (SH) waves propagating into a domain truncated by a wave‐absorbing boundary condition (WABC). By effectively training the CNN using input‐layer features (surface sensor measurements) and output‐layer features (effective forces at a DRM layer), we achieve significant reductions in processing time compared to PDE‐constrained optimization methods. The numerical experiments demonstrate the method's effectiveness and robustness in identifying effective forces, equivalent to incoherent incident waves, at a DRM layer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Maharjan,

Guidio,

Jeong

2023

Earthq Engng Struct Dyn

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“…Assuming that the critical vibration mode of the reactor vessel is at 20 Hz, the horizontal springs of the resonant metamaterials are tuned to horizontal vibration frequency f m = 19 Hz. As shown in [4], in this way they are expected to oscillate out-of-phase with the soil at the critical excitation frequency of 20 Hz, exerting opposite forces that reduce soil response, and, hence, the response of the structure.…”

Section: Resonant Metamaterialsmentioning

confidence: 99%

“…This is an encouraging result, given the fact that the total resonating mass of the metamaterials is only 4.3% of the NPP reactor building's mass. As shown in [4], to achieve a meaningful response reduction for the lower frequency vibration modes of a structure (near 2 Hz), the total metamaterial mass should be comparable to the mass of the structure. However, in the higher frequency range that is studied here, the participation mass of the structure is dramatically reduced compared to the lower-frequency vibration modes, allowing for a smaller metamaterial−reactor mass ratio of just 4.3%.…”

Section: Sh Wave Sweep Signal (1−35 Hz)mentioning

confidence: 99%

“…The advent of metamaterials-engineered materials/structures, with 'unnatural' properties (e.g., negative Poisson's ratio [1]), stemming either from their tailored geometric design or their resonant characteristics-has enabled researchers to manipulate the propagation of waves across the entire frequency spectrum [2]. In earthquake engineering, seismic (resonant or non-resonant) metamaterials can be used as metabarriers [3,4] or metafoundations [5], offering seismic protection against both body and surface seismic waves aiming to protect the lower frequency vibration modes of structures (i.e., 1−10 Hz).…”

Section: Introductionmentioning

confidence: 99%

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Resonant Metamaterials for high-frequency vibration mitigation in Nuclear Power Plants

Kanellopoulos,

Jeremic,

Anastasopoulos

et al. 2024

J. Phys.: Conf. Ser.

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The paper explores the potential effectiveness of horizontally vibrating resonant metamaterials to mitigate high-frequency vibrations of critical equipment/components caused by vertically propagating horizontal shear waves. These components are housed in stiff and strong structures of high importance, such as the reactor vessel inside the reactor building of Nuclear Power Plants (NPPs). High-fidelity 3D Finite Element (FE) models of the reactor building with the resonant metamaterials embedded into the soil on the sides of the NPP reactor building in a strip-like configuration (1 x 6 x 22) are developed in the Real-ESSI Simulator. After describing the FE model, its individual components are verified. The preliminary analysis shows that properly tuned resonant metamaterials (i.e., resonant frequency at 19-20 Hz) with a total vibrating mass of only 4.3% of the NPP reactor building mass, can lead to 20% reduction of the in-structure response spectrum at the centre of the foundation, where the reactor vessel is located, at the critical vibration frequency of the reactor vessel of 20 Hz. The amplification effect of the metamaterials at lower frequencies, stemming from their in-phase vibration with the soil, could be avoided if they are designed as semi-active or active systems that vibrate exclusively at the desired frequency. Finally, it is shown that the effect of metamaterials in this high frequency range is very localized, vanishing rapidly within a distance of one unit cell, indicating that their geometric configuration could be optimized to increase their efficiency.

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Mechanical Response of Four‐Star‐Honeycomb Hybrid Metamaterial Under In‐Plane Loading

Mwema,

Wambua,

Igwe

et al. 2024

Adv Eng Mater

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This paper focuses on designing and producing a hybrid metamaterial with a relative density of at least 0.59 using additive manufacturing. The metamaterial consists of layers of four‐star (A) and honeycomb (B)‐shaped unit cells. Three configurations (ABA, AABAA, and AABB) were 3D printed at various layer heights (0.06, 0.10, 0.15, and 0.20 mm). The quality of the printed samples depends on the layer height, with lower heights producing fewer defects and better geometric accuracy. In‐plane compression tests were conducted to evaluate the mechanical properties. The stress‐strain curves exhibited linear plateau and densification regions, varying across designs and layer heights. The AABB structure, printed at 0.1 and 0.06‐mm layer heights, showed the highest peak stress, while the ABA structure exhibited the lowest stress. The AABB structure, with a density of 742 kg/m³, demonstrated the potential for large deformation applications. Visual examination revealed distinct distortion patterns in the unit cells during loading, with the honeycomb cells in the ABA structure experiencing the most significant shape distortion. Overall, this research highlights the potential of hybrid metamaterials for lightweight and large deformation applications. The optimal design and manufacturing parameters can be tailored to achieve specific mechanical properties and performance requirements.

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Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil

Cited by 11 publications

References 38 publications

Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Resonant Metamaterials for high-frequency vibration mitigation in Nuclear Power Plants

Mechanical Response of Four‐Star‐Honeycomb Hybrid Metamaterial Under In‐Plane Loading

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