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
Publication: Journal of Engineering Mechanics
Volume 149, Issue 6
Abstract
We present a new convolutional neural network (CNN)-based element-wise classification method to detect a random number of voids with arbitrary shapes in a two-dimensional (2D) plane-strain solid subjected to elastodynamics. We consider that an elastic wave source excites the solid including a random number of voids, and wave responses are measured by sensors placed around the solid. We present a CNN for resolving the inverse problem, which is formulated as an element-wise classification problem. The CNN is trained to classify each element into a regular or void element from measured wave signals. Element-wise binary classification enables the identification of targeted voids of any shapes and any number without prior knowledge or hint about their locations, shape types, and numbers, while existing methods rely on such prior information. To this end, we generate training data consisting of input-layer features (i.e., measured wave signals at sensors) and output-layer features (i.e., element types of all elements). When the training data are generated, we utilize the level-set method to avoid an expensive remeshing process, which is otherwise needed for each different configuration of voids. We also analyze how effectively the CNN performs on blind test data from a non-level-set wave solver that explicitly models the boundary of voids using an unstructured, fine mesh. Numerical results show that the suggested approach can detect the locations, shapes, and sizes of multiple elliptical and circular voids in the 2D solid domain in the test data set as well as a blind test data set.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
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MATLAB code (.m format) of the presented forward modeling.
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MATLAB data sets (.mat format) of the presented numerical results.
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Tensorflow code (.py format) of the presented CNN modeling.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Award CMMI-2053694. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors are also grateful for the support by the Faculty Research and Creative Endeavors (FRCE) Research Grant-48058 at Central Michigan University. This paper is contribution # 181 of the Central Michigan University Institute for Great Lakes Research. The authors also greatly appreciate the reviewers’ very constructive comments, which substantially helped in improving the paper.
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© 2023 American Society of Civil Engineers.
History
Received: Jul 12, 2022
Accepted: Feb 8, 2023
Published online: Apr 8, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 8, 2023
ASCE Technical Topics:
- Arbitration
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Detection methods
- Dispute resolution
- Elastic analysis
- Engineering fundamentals
- Field tests
- Fluid mechanics
- Geomechanics
- Geotechnical engineering
- Hydrologic engineering
- Legal affairs
- Methodology (by type)
- Neural networks
- Nondestructive tests
- Practice and Profession
- Random waves
- Rock mechanics
- Structural analysis
- Structural engineering
- Tests (by type)
- Voids
- Water and water resources
- Waves (fluid mechanics)
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