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Prof. Dr. Piotr Lacki

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: materials sciences; metal forming; refill friction stir spot welding; numerical simulation; civil engineering; composite beams; genetic algorithms; neural networks
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Prof. Dr. Janina Adamus

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: metal forming; tribology; heat transfer through building partitions; artificial intelligence in technical solutions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Anna Derlatka

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Department of Civil Engineering, Faculty of Civil Engineering, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: artificial neural network; thin-walled structures; composite beams; steel-concrete composite structures; refill friction stir spot welding; numerical simulation

Prof. Dr. Wojciech Więckowski

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Department of Technology and Automation, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 69 Dąbrowskiego St., 42-201 Częstochowa, Poland
Interests: metal forming; sheet metal stamping; tribology; bioengineering; biomaterials; numerical simulation; friction stir welding; artificial neural network

Prof. Dr. Krzysztof Cpałka

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Institute of Computational Intelligence, Czestochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, Poland
Interests: artificial intelligence; fuzzy systems; population-based algorithms; neural networks; interpretability

Special Issue Information

Dear Colleagues,

The combination of Artificial Intelligence (AI) and Materials Science and Engineering gives rise to innovative approaches that accelerate the discovery, development and optimization of materials and technologies with improved properties. This constructive interaction holds immense promise for revolutionizing industries ranging from civil engineering to metal forming, ushering in a new era of material innovation.

AI plays a crucial role in predictive modeling, enabling researchers to simulate and understand the behavior of materials under various conditions. Machine learning algorithms analyze complex datasets to predict material responses to different external factors, such as temperature, pressure, or chemical exposure. This capability enhances our ability to design materials with tailored properties for specific applications. As we delve deeper into this interdisciplinary collaboration, the synergies between AI and Materials Science are expected to yield breakthroughs with far-reaching implications for diverse industries and technological advancements.

The objective of this Special Issue is to establish a knowledge platform that encourages researchers and engineers to advance research in the field of Materials Science and Engineering, employing the diverse applications of artificial intelligence.

This Special Issue invites the submission of manuscripts that explore the utilization of AI in Materials Science and Engineering, particularly concerning through classical and state-of-the-art manufacturing techniques. We encourage the submission of full papers on this subject.

Prof. Dr. Piotr Lacki
Prof. Dr. Janina Adamus
Prof. Dr. Anna Derlatka
Prof. Dr. Wojciech Więckowski
Prof. Dr. Krzysztof Cpałka
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.

Related Special Issues

Published Papers (1 paper)

Research

21 pages, 15097 KiB

Open AccessArticle

The Potential of Multi-Task Learning in CFDST Design: Load-Bearing Capacity Design with Three MTL Models

by Zhenyu Wang, Jian Zhou and Kang Peng

Materials 2024, 17(9), 1994; https://doi.org/10.3390/ma17091994 - 25 Apr 2024

Viewed by 174

Abstract

Concrete-filled double steel tubes (CFDSTs) are a load-bearing structure of composite materials. By combining concrete and steel pipes in a nested structure, the performance of the column will be greatly improved. The performance of CFDSTs is closely related to their design. However, existing codes for CFDST design often focus on how to verify the reliability of a design, but specific design parameters cannot be directly provided. As a machine learning technique that can simultaneously learn multiple related tasks, multi-task learning (MTL) has great potential in the structural design of CFDSTs. Based on 227 uniaxial compression cases of CFDSTs collected from the literature, this paper utilized three multi-task models (multi-task Lasso, VSTG, and MLS-SVR) separately to provide multiple parameters for CFDST design. To evaluate the accuracy of models, four statistical indicators were adopted (R², RMSE, RRMSE, and ρ). The experimental results indicated that there was a non-linear relationship among the parameters of CFDSTs. Nevertheless, MLS-SVR was still able to provide an accurate set of design parameters. The coefficient matrices of two linear models, multi-task Lasso and VSTG, revealed the potential connection among CFDST parameters. The latent-task matrix V in VSTG divided the prediction tasks of inner tube diameter, thickness, strength, and concrete strength into three groups. In addition, the limitations of this study and future work are also summarized. This paper provides new ideas for the design of CFDSTs and the study of related codes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Materials Science and Engineering)

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