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Numerical Simulation and Computational Methods in Engineering and Sciences, 2nd...
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Numerical Simulation and Computational Methods in Engineering and Sciences, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2039

Special Issue Editors

Prof. Dr. Zhuojia Fu

E-Mail Website
Guest Editor
College of Mechanics and Engineering Science, Hohai University, Nanjing 210098, China
Interests: acoustic and elastic waves; inverse problems; RBF-based meshless methods; Trefftz method; boundary element method
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yiqian He

E-Mail Website
Guest Editor
Department of Engineering Mechanics, Dalian University of Technology, Dalian 116024, China
Interests: computational mechanics; biomechanics for soft tissues; inverse problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hui Zheng

E-Mail Website
Guest Editor
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China
Interests: computational solid mechanics; biomechanics; phononic crystals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the past several decades, numerical simulation has been increasingly used as an important and powerful tool for solving science and engineering problems, thanks to the rapid development of computer technology and advanced computational methods. This Special Issue will present recent research results on numerical simulation and computational methods in engineering and sciences.

You are invited to submit original research or review papers to this Special Issue, which will include papers in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro) and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches and more efficient algorithms that will eventually make near-real-time computations possible are welcome. Original papers dealing with new methods such as meshless methods and mesh-reduction methods are also welcome.

Prof. Dr. Zhuojia Fu
Prof. Dr. Yiqian He
Prof. Dr. Hui Zheng
Guest Editors

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. Mathematics 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 2600 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

  • science and engineering problems
  • multi-physics problems
  • numerical simulation
  • computational methods
  • multiscale methods
  • advanced finite element methods
  • boundary element method
  • scaled boundary finite element method
  • meshless and particle methods
  • discrete element methods

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Related Special Issue

Published Papers (1 paper)

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Research

37 pages, 11643 KiB  
Article
Co-CrackSegment: A New Collaborative Deep Learning Framework for Pixel-Level Semantic Segmentation of Concrete Cracks
by Nizar Faisal Alkayem, Ali Mayya, Lei Shen, Xin Zhang, Panagiotis G. Asteris, Qiang Wang and Maosen Cao
Mathematics 2024, 12(19), 3105; https://doi.org/10.3390/math12193105 - 4 Oct 2024
Cited by 4 | Viewed by 1445
Abstract
In an era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation of existing infrastructure through health monitoring has emerged as [...] Read more.
In an era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation of existing infrastructure through health monitoring has emerged as an important challenge facing engineers. In recent years, intelligent approaches, such as data-driven machines and deep learning crack detection have gradually dominated over traditional methods. Among them, the semantic segmentation using deep learning models is a process of the characterization of accurate locations and portraits of cracks using pixel-level classification. Most available studies rely on single-model knowledge to perform this task. However, it is well-known that the single model might suffer from low variance and low ability to generalize in case of data alteration. By leveraging the ensemble deep learning philosophy, a novel collaborative semantic segmentation of concrete cracks method called Co-CrackSegment is proposed. Firstly, five models, namely the U-net, SegNet, DeepCrack19, DeepLabV3-ResNet50, and DeepLabV3-ResNet101 are trained to serve as core models for the ensemble model Co-CrackSegment. To build the ensemble model Co-CrackSegment, a new iterative approach based on the best evaluation metrics, namely the Dice score, IoU, pixel accuracy, precision, and recall metrics is developed. Results show that the Co-CrackSegment exhibits a prominent performance compared with core models and weighted average ensemble by means of the considered best statistical metrics. Full article
(This article belongs to the Special Issue Numerical Simulation and Computational Methods in Engineering and Sciences, 2nd Edition)
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