Fracture Analysis of Materials Based on Fractal Nature

A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Engineering".

Deadline for manuscript submissions: 25 March 2025 | Viewed by 1155

Special Issue Editors


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Guest Editor
Engineering Post Graduation Program, Federal University of Pampa (UNIPAMPA), Alegrete 97546-550, Brazil
Interests: fracture mechanics; fractals; concrete; numerical simulations

E-Mail Website
Guest Editor
Engineering Post Graduation Program, Federal University of Pampa (UNIPAMPA), Alegrete 97546-550, Brazil
Interests: fracture mechanics; fractal nature; numerical simulations; structural health monitoring

E-Mail Website
Guest Editor
School of Civil Engineering, Research Center of Large-Span Spatial Structures, Tianjin University, Tianjin 300350, China
Interests: fracture behavior; structural health monitoring and resilience; high-performance materials
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Special Issue Information

Dear Colleagues,

It has been observed that the fracture surface of metals, rock, concrete, and many other disordered or heterogeneous materials can be described by fractals characterized by random self-similarity, i.e., they exhibit statistically similar morphologies at different scales of observation. This aspect cannot be ignored or even replaced by a mean field when the fracture analysis of such materials occurs since many length scales interact during the material failure process. In this case, the fundamental character of the phenomenon from a physical and topological point of view can be represented by the fractal description of the material.

Furthermore, it can be explained that the behavior of a material depends on its microstructural disorder and its relation to its size at the macro scale. The microstructural disorder is a scale-independent material property less important when increasing the structural size. From a fractal point of view, this represents the change from a non-integer dimension to an integer dimension, that is, Euclidean space. Various researchers have used this idea to investigate concepts directly or indirectly related to the scale effect, fracture, acoustic emission, and fractality.

This Special Issue focuses on further advancing research on topics related to the fractal approach to the fracture analysis of materials using experimental testing, numerical simulation, and structural health monitoring. Topics that are invited for submission include (but are not limited to) the following:

  • Size effect based on the fractal theory;
  • Fractal analysis and its applications in fracture mechanics;
  • Applications of fractal approaches to fracture failure and damage of materials under different loading conditions;
  • Fractal/multi-fractal analysis for structural health monitoring.

Dr. Luis Eduardo Kosteski
Dr. Leandro Ferreira Friedrich
Prof. Dr. Jie Xu
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • size effect
  • fracture
  • damage
  • experimental analysis
  • acoustic emission
  • numerical simulations

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Published Papers (1 paper)

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Research

16 pages, 3461 KiB  
Article
Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning
by Wenhao Guo, Leiyang Zhong, Dejin Zhang and Qingquan Li
Fractal Fract. 2024, 8(8), 468; https://doi.org/10.3390/fractalfract8080468 - 12 Aug 2024
Viewed by 702
Abstract
Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to [...] Read more.
Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments. Full article
(This article belongs to the Special Issue Fracture Analysis of Materials Based on Fractal Nature)
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