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Applied Sciences
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Fracture Mechanics: From Theory to Applications
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Fracture Mechanics: From Theory to Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 5046

Special Issue Editor

Dr. Zhiyong Wang

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Interests: damage and fractures; impact; numerical methods

Special Issue Information

Dear Colleagues,

As the application field of fracture mechanics continues to grow, the demand for a more stable and efficient solution for the related problems in the field is also increasing, which could counteract the shortcomings of classical analytical and empirical solutions. Deep learning approaches provide a new paradigm for mechanics research, which is also a promising method for solving fracture problems, including crack growth modes, fatigue loading problems, fracture performance evaluation of new materials and dimensional analysis.

This Special Issue encourages and welcomes original research articles on the combination of traditional fracture problems and deep learning methods.

Potential topics include, but are not limited to, the following:

  • Research on crack propagation modes based on deep learning;
  • Research on crack stress fields of different materials based on deep learning;
  • Research on crack dynamic evolution processes based on deep learning;
  • Research on material fatigue loading problems based on deep learning;
  • Research on fracture performance evaluation of new materials based on deep learning;
  • Research on dimensional analysis of fracture mechanics based on deep learning.

Dr. Zhiyong Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

Published Papers (3 papers)

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Research

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14 pages, 3722 KiB  
Article
Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network
by Patchanida Seenuan, Nitikorn Noraphaiphipaksa and Chaosuan Kanchanomai
Appl. Sci. 2023, 13(20), 11446; https://doi.org/10.3390/app132011446 - 18 Oct 2023
Cited by 1 | Viewed by 1124
Abstract
During pipeline operation, internal cracks may occur. The severity around the crack tip can be quantified by the stress intensity factor (KI), which is a linear–elastic fracture mechanics parameter. For pressurized pipes featuring infinitely long internal surface cracks, KI [...] Read more.
During pipeline operation, internal cracks may occur. The severity around the crack tip can be quantified by the stress intensity factor (KI), which is a linear–elastic fracture mechanics parameter. For pressurized pipes featuring infinitely long internal surface cracks, KI can be interpolated from a function considering pressure, geometry, and crack size, as presented in API 579-1/ASME FFS-1. To enhance KI prediction accuracy, an artificial neural network (ANN) model was developed for such pressurized pipes. Predictions from the ANN model and API 579-1/ASME FFS-1 were compared with precise finite element analysis (FEA). The ANN model with an eight-neuron sub-layer outperformed others, displaying the lowest mean squared error (MSE) and minimal validation discrepancies. Nonlinear validation data improved both MSE and testing performance compared to uniform validation. The ANN model accurately predicted normalized KI, with differences of 2.2% or lower when compared to FEA results. Conversely, API 579-1/ASME FFS-1′s bilinear interpolation predicted inaccurately, exhibiting disparities of up to 4.3% within the linear zone and 24% within the nonlinearity zone. Additionally, the ANN model effectively forecasted the critical crack size (aC), differing by 0.59% from FEA, while API 579-1/ASME FFS-1′s bilinear interpolation underestimated aC by 4.13%. In summary, the developed ANN model offers accurate forecasts of normalized KI and critical crack size for pressurized pipes, providing valuable insights for structural assessments in critical engineering applications. Full article
(This article belongs to the Special Issue Fracture Mechanics: From Theory to Applications)
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17 pages, 3600 KiB  
Article
A Rapid Bridge Crack Detection Method Based on Deep Learning
by Yifan Liu, Weiliang Gao, Tingting Zhao, Zhiyong Wang and Zhihua Wang
Appl. Sci. 2023, 13(17), 9878; https://doi.org/10.3390/app13179878 - 31 Aug 2023
Cited by 2 | Viewed by 1150
Abstract
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images [...] Read more.
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concrete crack images were generated by a deep convolutional generative adversarial network (DCGAN), and the artificial samples were balanced and feature-rich. Then, the dataset was established by mixing the artificial samples with the original samples. You Only Look Once v5 (YOLOv5) was trained on this dataset to implement rapid detection of concrete bridge cracks, and the detection accuracy was compared with the results using only the original samples. The experiments show that DCGAN can mine the potential distribution of image data and extract crack features through the deep transposed convolution layer and down sampling operation. Moreover, the light-weight YOLOv5 increases channel capacity and reduces the dimensions of the input image without losing pixel information. This method maintains the generalization performance of the neural network and provides an alternative solution with a low cost of data acquisition while accomplishing the rapid detection of bridge cracks with high precision. Full article
(This article belongs to the Special Issue Fracture Mechanics: From Theory to Applications)
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Other

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13 pages, 3779 KiB  
Technical Note
Equivalent Stress Intensity Factor: The Consequences of the Lack of a Unique Definition
by Sérgio M. O. Tavares and Paulo M. S. T. de Castro
Appl. Sci. 2023, 13(8), 4820; https://doi.org/10.3390/app13084820 - 12 Apr 2023
Cited by 4 | Viewed by 2160
Abstract
The concept of an equivalent stress intensity factor Keq is used in the study of fatigue crack growth in mixed-mode situations. A problem seldom discussed in the research literature are the consequences of the coexistence of several alternative definitions of mixed mode [...] Read more.
The concept of an equivalent stress intensity factor Keq is used in the study of fatigue crack growth in mixed-mode situations. A problem seldom discussed in the research literature are the consequences of the coexistence of several alternative definitions of mixed mode Keq, leading to rather different results associated with the alternative Keq definitions. This note highlights the problem, considering several Keq definitions hitherto not analyzed simultaneously. Values of Keq calculated according to several criteria were compared through the determination of Keq/KI over a wide range of values of KI/KII or KII/KI. In earlier work on Al alloy AA6082 T6, the fatigue crack path and growth rate were measured in 4-point bend specimens subjected to asymmetrical loading and in compact tension specimens modified with holes. The presentation of the fatigue crack growth data was made using a Paris law based on Keq. Important differences are found in the Paris laws, corresponding to the alternative definitions of Keq considered, and the requirements for candidate Keq definitions are discussed. A perspective for overcoming the shortcomings may consist in developing a data-driven modelling methodology, supported by material characterization and structure monitoring during its life cycle. Full article
(This article belongs to the Special Issue Fracture Mechanics: From Theory to Applications)
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