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Artificial Intelligence Instruments Applied in Materials Science

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 2604

Special Issue Editor


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Guest Editor
“Gheorghe Asachi” Technical University, Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Iasi, Romania
Interests: modelling; optimization; artificial intelligence; artificial neural networks; evolutionary algorithms

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) tools are proving useful in various fields, the best known techniques being neural networks, for regression and classification problems, and evolutionary algorithms, for optimization problems. Modelling with neural networks is recommended in situations where the phenomenology of a process is not precisely known, but input–output data are available, reflecting the dependence between variables. Evolutionary algorithms offer a wide range of methods that can be applied to optimize processes (systems) or models developed for them. Obviously, AI offers other effective tools, inspired by the behavior of humans, animals, or various physical phenomena. Another important aspect is related to the possibility of combining these techniques in hybrid soft-computing configurations that integrate the advantages of the individual techniques. In the field of materials modelling and optimization (and others), AI techniques may allow both materials synthesis and the study of structure–properties relationships.

Topics of interest of this Special Issue include, but are not limited to:

  • Modelling with neural networks
  • Optimization with evolutionary algorithms
  • Hybrid AI techniques
  • Other AI activities and techniques applied in the field of materials

Prof. Silvia Curteanu
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

  • Modelling
  • Optimization
  • Artificial Intelligence
  • Neural networks
  • Evolutionary algorithms
  • Hybrid soft-computing techniques
  • Material synthesis modelling and optimization
  • Material properties modelling and optimization

Published Papers (1 paper)

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Research

16 pages, 5191 KiB  
Article
Using Artificial Intelligence Techniques to Predict Punching Shear Capacity of Lightweight Concrete Slabs
by Ahmed Ebid and Ahmed Deifalla
Materials 2022, 15(8), 2732; https://doi.org/10.3390/ma15082732 - 7 Apr 2022
Cited by 24 | Viewed by 1974
Abstract
Although lightweight concrete is implemented in many mega projects to reduce the cost and improve the project’s economic aspect, research studies focus on investigating conventional normal-weight concrete. In addition, the punching shear failure of concrete slabs is dangerous and calls for precise and [...] Read more.
Although lightweight concrete is implemented in many mega projects to reduce the cost and improve the project’s economic aspect, research studies focus on investigating conventional normal-weight concrete. In addition, the punching shear failure of concrete slabs is dangerous and calls for precise and consistent prediction models. Thus, this current study investigates the prediction of the punching shear strength of lightweight concrete slabs. First, an extensive experimental database for lightweight concrete slabs tested under punching shear loading is gathered. Then, effective parameters are determined by applying the principles of statistical methods, namely, concrete density, columns dimensions, slab effective depth, concrete strength, flexure reinforcement ratio, and steel yield stress. Next, the manuscript presented three artificial intelligence models, which are genetic programming (GP), artificial neural network (ANN) and evolutionary polynomial regression (EPR). In addition, it provided guidance for future design code development, where the importance of each variable on the strength was identified. Moreover, it provided an expression showing the complicated inter-relation between affective variables. The novelty lies in developing three proposed models for the punching capacity of lightweight concrete slabs using three different (AI) techniques capable of accurately predicting the strength compared to the experimental database Full article
(This article belongs to the Special Issue Artificial Intelligence Instruments Applied in Materials Science)
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