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Artificial Intelligence in Advanced Materials and Structures
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Artificial Intelligence in Advanced Materials and Structures

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

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 6886

Special Issue Editor

Dr. Pengcheng Jiao

E-Mail Website
Guest Editor
Institute of Port, Coastal and Offshore Engineering, Ocean College, Zhejiang University, Zhoushan 316021, Zhejiang, China
Interests: artificial intelligence in engineering; mechanical metamaterials; architected structures; structural health monitoring (SHM); energy harvesting; multiscale solid mechanics; structural instability analysis; experimental and computational mechanics; marine soft robotics

Special Issue Information

Dear Colleagues,

Materials and structures have experienced a rapid development in recent years and are estimated to remain at the top spot in the fields of materials science and structural engineering among researchers, engineers, or technical supporters over the next several decades. However, technical challenges have been associated with the development of structures and materials science, which have hampered the control of advanced structures and exploration of innovative materials. This issue has been exacerbated by the spread of interdisciplinary materials. Next-generation materials and structures will be significantly dependent on the data-driven intelligent methods integrated with information and communication techniques. Aiming to explore the understanding on material characteristics and structural performance, artificial intelligence (AI) is arguably the most vital component of advanced materials and structures, attributed to its efficiency in predicting material properties, optimizing structural response, discovering new mechanisms beyond intuitions, etc. 

This Special Issue will compile recent applications of AI technologies in advanced materials and structures and is expected to cover but not be limited to the following topics: advanced structural materials; AI in engineering, materials science, and structural analysis; high-performance functional materials; self-sensing materials and structures; smart materials and structures for extreme events and multiple hazard scenarios; and structural optimization and inverse design. This Special Issue may open to other topics broadly related to advanced materials and structures designed, optimized, or expanded by AI technologies.

Dr. Pengcheng Jiao
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. 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

  • advanced structural materials
  • artificial intelligence in engineering
  • artificial intelligence in materials science
  • artificial intelligence in structural analysis
  • high-performance functional materials
  • self-sensing materials and structures
  • smart materials and structures for extreme events
  • smart materials and structures for multiple hazard scenarios
  • structural optimization and inverse design

Published Papers (3 papers)

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Research

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32 pages, 13224 KiB  
Article
Application of KNN and ANN Metamodeling for RTM Filling Process Prediction
by Boon Xian Chai, Boris Eisenbart, Mostafa Nikzad, Bronwyn Fox, Ashley Blythe, Kyaw Hlaing Bwar, Jinze Wang, Yuntong Du and Sergey Shevtsov
Materials 2023, 16(18), 6115; https://doi.org/10.3390/ma16186115 - 7 Sep 2023
Cited by 5 | Viewed by 996
Abstract
Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and [...] Read more.
Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Materials and Structures)
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18 pages, 5842 KiB  
Article
Optimization-Based Economical Flexural Design of Singly Reinforced Concrete Beams: A Parametric Study
by Rizwan Azam, Muhammad Rizwan Riaz, Muhammad Umer Farooq, Faraz Ali, Muhammad Mohsan, Ahmed Farouk Deifalla and Abdeliazim Mustafa Mohamed
Materials 2022, 15(9), 3223; https://doi.org/10.3390/ma15093223 - 29 Apr 2022
Cited by 12 | Viewed by 2437
Abstract
In the past, many studies have been conducted on the optimization of reinforced concrete (RC) structures. These studies have demonstrated the effectiveness of different optimization techniques to obtain an economical design. However, the use of optimization techniques to an obtain economical design is [...] Read more.
In the past, many studies have been conducted on the optimization of reinforced concrete (RC) structures. These studies have demonstrated the effectiveness of different optimization techniques to obtain an economical design. However, the use of optimization techniques to an obtain economical design is not so practical due to the difficulty in applying most of the optimization techniques to achieve an optimal solution. The RC beam is one of the most common structural elements encountered by a practising design engineer. The current study is designed to highlight the potential of the Solver tool in MS Excel as an easy-to-use option for optimizing the design of simply supported RC beams. A user-friendly interface was developed in a spreadsheet in which beam design parameters from a typical design can be entered and an economical design can be obtained using the Evolutionary Algorithm available in the MS Excel Solver tool. To demonstrate the effectiveness of the developed optimization tool, three examples obtained from the literature have been optimized. The results showed that up to 24% economical solution can be obtained by keeping the same material strengths that were assumed in the original design. However, if material strength is also considered as a variable, up to 44% of the economical solution can be obtained. A parametric study was also conducted to investigate the effect of different design variables on the economical design of simply supported RC beams and to derive useful rules of thumb for their design and proportioning, with the objective of cost minimization. The results of the parametric study suggest that the grade of the reinforcing steel is one of the most influential factors that affect the cost of simply supported RC beams. Practicing engineers can use the trends derived from this research to further refine their optimal designs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Materials and Structures)
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Review

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22 pages, 12042 KiB  
Review
Emerging Deep-Sea Smart Composites: Advent, Performance, and Future Trends
by Haiyi Zhou, Pengcheng Jiao and Yingtien Lin
Materials 2022, 15(18), 6469; https://doi.org/10.3390/ma15186469 - 17 Sep 2022
Cited by 2 | Viewed by 2495
Abstract
To solve the global shortage of land and offshore resources, the development of deep-sea resources has become a popular topic in recent decades. Deep-sea composites are widely used materials in abyssal resources extraction, and corresponding marine exploration vehicles and monitoring devices for deep-sea [...] Read more.
To solve the global shortage of land and offshore resources, the development of deep-sea resources has become a popular topic in recent decades. Deep-sea composites are widely used materials in abyssal resources extraction, and corresponding marine exploration vehicles and monitoring devices for deep-sea engineering. This article firstly reviews the existing research results and limitations of marine composites and equipment or devices used for resource extraction. By combining the research progress of smart composites, deep-sea smart composite materials with the three characteristics of self-diagnosis, self-healing, and self-powered are proposed and relevant studies are summarized. Finally, the review summarizes research challenges for the materials, and looks forward to the development of new composites and their practical application in conjunction with the progress of composites disciplines and AI techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence in Advanced Materials and Structures)
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