scholarly journals Geometric Attributes of Polygonal Crack Patterns in Columnar Joints

2021 ◽  
Author(s):  
Y. Akiba ◽  
A. Takashima ◽  
A. Inoue ◽  
H. Ishidaira ◽  
H. Shima
2020 ◽  
Author(s):  
Yuri Akiba ◽  
Aika Takashima ◽  
Akio Inoue ◽  
Hiroshi Ishidaira ◽  
Hiroyuki Shima

Author(s):  
Lucas Goehring

When cracks form in a thin contracting layer, they sequentially break the layer into smaller and smaller pieces. A rectilinear crack pattern encodes information about the order of crack formation, as later cracks tend to intersect with earlier cracks at right angles. In a hexagonal pattern, in contrast, the angles between all cracks at a vertex are near 120°. Hexagonal crack patterns are typically seen when a crack network opens and heals repeatedly, in a thin layer, or advances by many intermittent steps into a thick layer. Here, it is shown how both types of pattern can arise from identical forces, and how a rectilinear crack pattern can evolve towards a hexagonal one. Such an evolution is expected when cracks undergo many opening cycles, where the cracks in any cycle are guided by the positions of cracks in the previous cycle but when they can slightly vary their position and order of opening. The general features of this evolution are outlined and compared with a review of the specific patterns of contraction cracks in dried mud, polygonal terrain, columnar joints and eroding gypsum–sand cements.


2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.


1991 ◽  
Vol 37 (127) ◽  
pp. 319-322 ◽  
Author(s):  
E. M. Schulson ◽  
W. D. Hibler
Keyword(s):  

AbstractFrom observations and calculations of crack patterns in ice, it is suggested that a similar mechanism may account for cracking over a wide range of scales.


2004 ◽  
Vol 289 (3) ◽  
pp. 227-237 ◽  
Author(s):  
Lothar Weh ◽  
Astrid Venthur

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