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Multiscale Analysis of Advanced Fiber Materials and Structures
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Multiscale Analysis of Advanced Fiber Materials and Structures

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Composites".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 4920

Special Issue Editors

Prof. Dr. Xiaoyi Zhou

E-Mail Website
Guest Editor
Department of Bridge Engineering, School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 200089, China
Interests: fiber-reinforced polymer composites; cellular materials; multiscale analysis; uncertainty quantification; design optimization; composite structures
Prof. Dr. Haohui Xin

E-Mail Website
Guest Editor
Department of Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: fiber-reinforced polymer composites; 3D-printed steel; multiscale analysis; failure and fatigue evaluation of steel and composite structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fiber-reinforced composites have been successfully applied in many industry sectors over the last few decades because of their excellent strength-to-weight ratio, durability, and technical advantages, and they are now spreading into more fields. In general, fiber-reinforced composites are used to replace metallic materials in harsh and complicated environments, where metallic materials suffer from durability, fatigue, and corrosion issues. To model fiber materials and structures with the aim of understanding their behaviors and failure mechanisms (in which internal length scales are not negligible when compared to structural length scales), multiscale analysis should be utilized to consider interactions among constituent materials to fully explore how constituents are used. Studies on the design of fiber materials and structures are also of paramount importance.

Due to their heterogeneity and the complex compositions or microstructures of their constituents, the use of appropriate multiscale methods to characterize fiber materials is greatly necessary. Existing methods have been found efficient in predicting elastic properties, but the prediction of material strengths and long-term behavior is still an unresolved issue due to the complex failure mechanisms of these materials. The utilization of multiscale methods also relies on how efficiently composite microstructures are modelled. In recent years, the characterization and analysis of advanced materials has become fundamental for predicting structural behavior, and to better design and utilize these materials.

The present Special Issue focuses on theoretical and experimental methods for the multiscale analysis of advanced fiber-reinforced composite materials and structures. As far as materials are concerned, we are interested in anisotropic, nonlocal, lattice and multi-physics behaviors. In addition, this Special Issue aims to attract contributions on multi-scale structural modelling modeling, 3D-printed structures and computer-aided structural engineering.

Prof. Dr. Xiaoyi Zhou
Prof. Dr. Haohui Xin
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. Materials 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

  • fiber reinforced polymer composites
  • particle reinforced polymer composites
  • fiber reinforced concrete
  • tow-steering fibre reinforced polymer composites
  • natural fibre reinforced polymer composites
  • elastic properties
  • thermal properties
  • strengths
  • formability
  • mechanical loads
  • thermal loads
  • homogenization
  • machine learning
  • scanning
  • microstructure modelling/reconstruction

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Published Papers (3 papers)

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Research

23 pages, 10167 KiB  
Article
A Metamodel-Based Multi-Scale Reliability Analysis of FRP Truss Structures under Hybrid Uncertainties
by Desheng Zhao, Xiaoyi Zhou and Wenqing Wu
Materials 2024, 17(1), 29; https://doi.org/10.3390/ma17010029 - 20 Dec 2023
Cited by 1 | Viewed by 1024
Abstract
This study introduces a Radial Basis Function-Genetic Algorithm-Back Propagation-Importance Sampling (RBF-GA-BP-IS) algorithm for the multi-scale reliability analysis of Fiber-Reinforced Polymer (FRP) composite structures. The proposed method integrates the computationally powerful RBF neural network with GA, BP neural network and IS to efficiently calculate [...] Read more.
This study introduces a Radial Basis Function-Genetic Algorithm-Back Propagation-Importance Sampling (RBF-GA-BP-IS) algorithm for the multi-scale reliability analysis of Fiber-Reinforced Polymer (FRP) composite structures. The proposed method integrates the computationally powerful RBF neural network with GA, BP neural network and IS to efficiently calculate inner and outer optimization problems for reliability analysis with hybrid random and interval uncertainties. The investigation profoundly delves into incorporating both random and interval parameters in the reliability appraisal of FRP constructs, ensuring fluctuating parameters within designated boundaries are meticulously accounted for, thus augmenting analytic exactness. In application, the algorithm was subjected to diverse structural evaluations, including a seven-bar planar truss, an architectural space dome truss, and an intricate nonlinear truss bridge. Results demonstrate the algorithm’s exceptional performance in terms of model invocation counts and accurate failure probability estimation. Specifically, within the seven-bar planar truss evaluation, the algorithm exhibited a deviation of 0.08% from the established failure probability benchmark. Full article
(This article belongs to the Special Issue Multiscale Analysis of Advanced Fiber Materials and Structures)
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18 pages, 9293 KiB  
Article
Curing Mechanisms of Polymeric Nano-Copolymer Subgrade
by Shuang Shi, Miao Wang, Linhao Gu, Xiang Chen and Yanning Zhang
Materials 2023, 16(12), 4316; https://doi.org/10.3390/ma16124316 - 11 Jun 2023
Viewed by 1422
Abstract
The mechanical properties of the subgrade have a significant impact on the service life and pavement performance of the superstructure of pavement. By adding admixtures and via other means to strengthen the adhesion between soil particles, the strength and stiffness of the soil [...] Read more.
The mechanical properties of the subgrade have a significant impact on the service life and pavement performance of the superstructure of pavement. By adding admixtures and via other means to strengthen the adhesion between soil particles, the strength and stiffness of the soil can be improved to ensure the long-term stability of pavement structures. In this study, a mixture of polymer particles and nanomaterials was used as a curing agent to examine the curing mechanism and mechanical properties of subgrade soil. Using microscopic experiments, the strengthening mechanism of solidified soil was analyzed with scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XDR). The results showed that with the addition of the curing agent, small cementing substances on the surface of soil minerals filled the pores between minerals. At the same time, with an increase in the curing age, the colloidal particles in the soil increased, and some of them formed large aggregate structures that gradually covered the surface of the soil particles and minerals. By enhancing the cohesiveness and integrity between different particles, the overall structure of the soil became denser. Through pH tests, it was found that the age had a certain effect on the pH of solidified soil, but the effect was not obvious. Through the comparative analysis of elements in plain soil and solidified soil, it was found that no new chemical elements were produced in the solidified soil, indicating that the curing agent does not have negative impacts on the environment. Full article
(This article belongs to the Special Issue Multiscale Analysis of Advanced Fiber Materials and Structures)
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15 pages, 3473 KiB  
Article
Bolt-Loosening Detection Using 1D and 2D Input Data Based on Two-Stream Convolutional Neural Networks
by Xiaoli Hou, Weichao Guo, Shengjie Ren, Yan Li, Yue Si and Lizheng Su
Materials 2022, 15(19), 6757; https://doi.org/10.3390/ma15196757 - 29 Sep 2022
Cited by 10 | Viewed by 1582
Abstract
At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images [...] Read more.
At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images and can precisely detect the bolt-loosening states. In this paper, the LeNet-5 network is improved by adjusting the size and number of the convolution kernels, introducing the dropout operation, and building a two-dimensional convolutional neural network (2DCNN) model. Combining the advantages of a one-dimensional convolutional neural network (1DCNN) with wide first-layer kernels to suppress high-frequency noise, a two-stream convolutional neural network (TSCNN) is proposed based on 1D and 2D input data. The proposed model uses raw vibration signals and time-frequency images as input and automatically extracts sensitive features and representative information. Finally, the effectiveness and superiority of the proposed approach are verified by practical experiments that are carried out on a machine tool guideway. The experimental results show that the proposed approach can effectively achieve end-to-end bolt-loosening fault diagnoses, with an average recognition accuracy of 99.58%. In addition, the method can easily achieve over 93% accuracy when the SNR is over 0 dB without any denoising preprocessing. The results show that the proposed approach not only achieves high classification accuracy but also has good noise immunity. Full article
(This article belongs to the Special Issue Multiscale Analysis of Advanced Fiber Materials and Structures)
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