Zhang, Pei, Fan, Weiying, Chen, Yao, Feng, Jian and Sareh, Pooya 
ORCID: 0000-0003-1836-2598
(2022)
Structural symmetry recognition in planar structures using Convolutional Neural Networks.
Engineering Structures, 260.
p. 114227.
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1-s20-S014102962200356X-main_AcceptedVersion.pdf - Author Accepted Manuscript Download (8MB) | Preview |
Abstract
In both natural and man-made structures, symmetry provides a range of desirable properties such as uniform distributions of internal forces, concise transmission paths of forces, as well as rhythm and beauty. Most research on symmetry focus on natural objects to promote the developments in computer vision. However, countless engineering structures also contain symmetry elements since ancient times. In fact, many scholars have investigated symmetry in engineering structures, but most of them are based on analytical methods which require tedious calculations. Inspired by the application of deep learning in image identification, in this paper, we use two Convolutional Neural Networks (CNNs) to respectively identify the symmetry group and symmetry order of planar engineering structures. To this end, two different datasets with labels for symmetric structures are created. Then, the datasets are used to train and test the constructed network models. For symmetry classification, it achieves 86.69% accuracy, which takes about 0.006 s to predict one picture. On the other hand, for symmetry order recognition, it reaches 92% accuracy, which expends about 0.005 s to identify an image. This method provides an efficient approach to the exploration of structural symmetry, which can be expanded and developed further toward the identification of symmetry in three-dimensional structures.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Deep learning, Planar structure, Pictures, Symmetry classification, Symmetry order |
| Divisions: | Faculty of Science and Engineering > School of Engineering |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 21 Apr 2022 07:52 |
| Last Modified: | 05 Apr 2023 01:30 |
| DOI: | 10.1016/j.engstruct.2022.114227 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3153511 |
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