Level-Set and Learn: Convolutional Neural Network for Classification of Elements to Identify an Arbitrary Number of Voids in a 2D Solid Using Elastic Waves

Pranto, Fazle Mahdi; Maharjan, Shashwat; Jeong, Chanseok

doi:10.1061/jenmdt.emeng-6840

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…We present our approach to process the input data with dimensions of 500 × 31 using a CNN. The initial step involves passing the input data through our first convolutional layer, wherein the convolutional operation employs a sliding dot product mechanism, 43 as illustrated in Figure 5. This layer comprises 480 filters, each possessing a filter size of 80.…”

Section: Example 1: a Truncated Domain With A Homogeneous Soil (Site ...mentioning

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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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: Example 1: a Truncated Domain With A Homogeneous Soil (Site ...mentioning

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

Self Cite

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Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

Kim,

Maharjan,

Pranto

et al. 2024

Ultrasonics

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Level-Set and Learn: Convolutional Neural Network for Classification of Elements to Identify an Arbitrary Number of Voids in a 2D Solid Using Elastic Waves

Cited by 2 publications

References 32 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

Convolutional neural network and level-set spectral element method for ultrasonic imaging of delamination cavities in an anisotropic composite structure

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