Structural symmetry recognition in planar structures using Convolutional Neural Networks. (1st June 2022)

• Record Type: Journal Article
• Authors: Zhang, Pei; Fan, Weiying; Chen, Yao; Feng, Jian; Sareh, Pooya
• Is Part Of: Engineering structures. Volume 260(2022)
• Publication Date: 2022-06-01
• Subjects: Deep learning -- Planar structure -- Pictures -- Symmetry classification -- Symmetry order; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114227
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A convolutional neural network recognizing symmetry of planar structures is established. Automated symmetry identification is converted to classification problem of pictures. The proposed method is robust for structures with C n or C nv symmetry. 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.