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Special Issue "Machine Learning Techniques in Materials Science and Engineering"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 5758

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Prof. Dr. Panagiotis G. Asteris

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Guest Editor

Computational Mechanics Laboratory, Department of Civil Engineering, School of Pedagogical and Technological Education, Athens, Greece
Interests: applied mathematics and numerical methods; computational mechanics; artificial neural networks; masonry structures
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Dr. Ahmed Salih Mohammed

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Guest Editor

1. College of Engineering, University of Sulaimani, Kurdistan Region, Sulaimani-Kirkuk Rd, Sulaymaniyah 46001, Iraq
2. College of Engineering, American University of Iraq, Sulaimani, Kurdistan region, Sulaimani-Kirkuk Rd, Sulaymaniyah 46001, Iraq
Interests: cement; concrete; soil mechanics; rock mechanics; sustainability; modeling
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Special Issue Information

Dear Colleagues,

Numerous modeling approaches using non-conventional adsorbents have been used to develop suitable and more effective adsorbents to eliminate and optimize experimental lab work. Since soft computing techniques are needed in composite materials engineering, they could be used in different phases of materials engineering. It is especially related to functional analysis, design, testing, prediction, and optimization. Neural networks (NNs), fuzzy logic, and evolutionary and classification algorithms are the most popular soft-computing techniques.

Articles submitted to this journal can also be concerned about the most significant recent developments in computational and numerical methods and their applications in structural engineering and Materials Engineering, including nano-sized and smart materials. We invite researchers to contribute original research articles and review articles that will stimulate the continuing research effort on applications of the soft computing approaches to model structural engineering and materials problems.

Prof. Dr. Panagiotis G. Asteris
Dr. Ahmed Salih Mohammed
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

artificial neural networks (ANNs)
computational biology/bioinformatics
computational science and engineering
evolutionary multimodal optimization
forecasting models
fuzzy set theory and hybrid fuzzy models
genetic algorithm and genetic programming
heuristic models
hybrid intelligent systems
image processing and computer vision
machine learning techniques
multicriteria decision making (MCDM)
multiexpression programming

Published Papers (5 papers)

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Research

Open AccessArticle

Estimation of Cyclic Stress–Strain Curves of Steels Based on Monotonic Properties Using Artificial Neural Networks

Tea Marohnić

Robert Basan

and

Ela Marković

Materials 2023, 16(14), 5010; https://doi.org/10.3390/ma16145010 - 15 Jul 2023

Viewed by 465

Abstract

This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress–strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress–strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg–Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress–strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model. Full article

(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)

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Open AccessArticle

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests

Yuzhen Wang

Mahdi Hasanipanah

Ahmad Safuan A. Rashid

Binh Nguyen Le

and

Dmitrii Vladimirovich Ulrikh

Materials 2023, 16(10), 3731; https://doi.org/10.3390/ma16103731 - 15 May 2023

Viewed by 687

Abstract

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson’s Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values. Full article

(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)

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Open AccessArticle

Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography

Mihai Iovea

and

Materials 2023, 16(4), 1654; https://doi.org/10.3390/ma16041654 - 16 Feb 2023

Viewed by 682

Abstract

In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20–160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values. Full article

(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)

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Open AccessEditor’s ChoiceArticle

Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks

and

Materials 2023, 16(2), 826; https://doi.org/10.3390/ma16020826 - 14 Jan 2023

Cited by 3 | Viewed by 2151

Abstract

Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model. Full article

(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)

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Open AccessArticle

Determination of TTT Diagrams of Ni-Al Binary Using Neural Networks

Leonardo Hernández-Flores

Angel-Iván García-Moreno

Enrique Martínez-Franco

Guillermo Ronquillo-Lomelí

and

Jhon Alexander Villada-Villalobos

Materials 2022, 15(24), 8767; https://doi.org/10.3390/ma15248767 - 08 Dec 2022

Viewed by 974

Abstract

The heat treatment of a metal is a set of heating and cooling cycles that a metal undergoes to change its microstructure and, therefore, its properties. Temperature–time–transformation (TTT) diagrams are an essential tool for interpreting the resulting microstructures after heat treatments. The present [...] Read more.

γ^{'}

phase for the Ni-Al alloy using artificial neural networks (ANNs). The proposed methodology is composed of five stages: (1) database creation, (2) experimental design, (3) ANNs training, (4) ANNs validation, and (5) proposed models analysis. Two approaches were addressed, the first to predict only the nose point of the TTT diagrams and the second to predict the complete curve. Finally, the best models for each approach were merged to compose a more accurate hybrid model. The results show that the multilayer perceptron architecture is the most efficient and accurate compared to the simulated TTT diagrams. The prediction of the nose point and the complete curve showed an accuracy of

98.07

% and

86.41

%, respectively. The proposed final hybrid model achieves an accuracy of

96.59

%. Full article

(This article belongs to the Special Issue Machine Learning Techniques in Materials Science and Engineering)

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