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Applied Sciences
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Novel Computing Methods for Machine Learning
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Novel Computing Methods for Machine Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (1 October 2021) | Viewed by 7667

Special Issue Editor

Dr. Khader M. Hamdia

E-Mail Website
Guest Editor
Department of Mathematics and Physics, Leibniz Universität Hannover, Hanover, Lower Saxony‎, Germany
Interests: numerical approximation; computational mechanics; machine learning; deep neural network; data-driven modeling; uncertainty quantification

Special Issue Information

Dear Colleagues,

Machine learning (ML) methods have been used in a wide range of applied sciences. These methods fundamentally are based on computational intelligence and employ statistical techniques to give the computers the ability to “learn” and recognize patterns from data. The algorithm of ML models mimics the function of the biological brain using training data to draw predictions and make decisions for new unseen data during the learning process. ML has been the subject of interest and is increasingly developed to support uncertainty quantification, modeling, and design optimization problems.

This Special Issue will focus on the most recent applications and developments of ML algorithms employing novel approaches in science and engineering. The topics of interest for publication include but are not limited to:

  • Big data analysis and pattern recognition.
  • Data-driven modeling techniques.
  • Novel machine learning algorithms.
  • Optimal architectures of deep learning methods.
  • Ensemble of optimizers for training and hybrid models.
  • Active learning methods for reliability analysis.
  • Machine learning in different applications (e.g.: engineering, medicine, material science, energy, optimization, decision making, etc.).

Dr. Khader M. Hamdia
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. Applied Sciences 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 2400 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

  • machine learning
  • deep learning
  • artificial neural networks
  • computational science
  • data-driven models
  • design optimization
  • reliability analysis

Published Papers (2 papers)

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Research

15 pages, 3351 KiB  
Article
Optimum Design of Flexural Strength and Stiffness for Reinforced Concrete Beams Using Machine Learning
by Nazim Abdul Nariman, Khader Hamdia, Ayad Mohammad Ramadan and Hamed Sadaghian
Appl. Sci. 2021, 11(18), 8762; https://doi.org/10.3390/app11188762 - 20 Sep 2021
Cited by 16 | Viewed by 2939
Abstract
In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning was applied to predict the responses of the structural system in three-point flexure tests. Three design input variables, [...] Read more.
In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning was applied to predict the responses of the structural system in three-point flexure tests. Three design input variables, the area of steel bars in the compression zone, the area of steel bars in the tension zone, and the area of steel bars in the shear zone, were adopted for the dataset and arranged by the Box-Behnken design method. The dataset was composed of thirteen specimens of reinforced concrete beams. The specimens were tested under three-point flexure loading at the age of 28 days and both the failure load and the maximum deflection values were recorded. Compression and tension tests were conducted to obtain the concrete data for the analysis and numerical modeling. Afterward, finite element modeling was performed for all the specimens using the ATENA program to verify the experimental tests. Subsequently, the surrogate models for the flexural strength and the stiffness were constructed. Finally, optimization was conducted supporting on the factorial method for the predicted responses. The adopted approach proved to be an excellent tool to optimize the design of reinforced concrete beams for flexure and stiffness. In addition, experimental and numerical results were in very good agreement in terms of both the failure type and the cracking pattern. Full article
(This article belongs to the Special Issue Novel Computing Methods for Machine Learning)
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22 pages, 6151 KiB  
Article
Feed-Forward Neural Networks for Failure Mechanics Problems
by Fadi Aldakheel, Ramish Satari and Peter Wriggers
Appl. Sci. 2021, 11(14), 6483; https://doi.org/10.3390/app11146483 - 14 Jul 2021
Cited by 30 | Viewed by 3721
Abstract
This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward [...] Read more.
This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced. Full article
(This article belongs to the Special Issue Novel Computing Methods for Machine Learning)
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