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Processes
Special Issues
Machine Learning Models for Sustainable Composite Materials
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Machine Learning Models for Sustainable Composite Materials

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Sustainable Processes".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 665

Special Issue Editors

Dr. Celal Cakiroglu

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Guest Editor
GameAbove College of Engineering and Technology, Eastern Michigan University, Ypsilanti, MI, USA
Interests: machine learning; composite materials; optimization
Prof. Dr. Zong Woo Geem

E-Mail Website
Guest Editor
Department of Smart City, Gachon University, Seongnam, Republic of Korea
Interests: optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gebrail Bekdaş

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Guest Editor
Civil Engineering Department, Istanbul University-Cerrahpasa, 34320 Istanbul, Turkey
Interests: optimization; machine learning; structural control; energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Composite materials have found broad application in aerospace engineering, vehicle design and construction sectors due to their beneficial light weight and strength properties. Furthermore, recent developments in machine learning technology have opened up new possibilities to further optimize the design of composite structures.

This Special Issue welcomes original research articles that advance the state of the art in data-driven modeling of sustainable composites. We invite papers related, but not limited to, the following research areas:

  • Numerical simulation of composite structures.
  • Predictive and statistical modeling of composites.
  • Sustainability of concrete.
  • Reinforced concrete structures.
  • Structural retrofitting with fiber-reinforced composites.
  • Dynamic response of composites under impact loading.
  • Composites in armor design.
  • Lightweight design with composites.
  • Optimization techniques and their applications to composites.
  • Modeling and simulation of laminated composites.
  • Buckling and dynamic response of thin-walled structures.
  • Composites made of natural fibers.
  • Analysis of fiber–matrix interface and bond strength.
  • Computational modeling of fatigue life and fracture toughness.
  • Behavior of composites under thermal stresses.
  • Composites in energy-efficient building design.

This Special Issue also welcomes experimental research papers, as machine learning models heavily rely on experimental data. Emphasis will be placed on studies that demonstrate how machine learning can enhance the sustainability and performance of composite structures. We look forward to your contributions.

Dr. Celal Cakiroglu
Prof. Dr. Zong Woo Geem
Prof. Dr. Gebrail Bekdaş
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 250 words) can be sent to the Editorial Office for assessment.

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. Processes is an international peer-reviewed open access monthly 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

  • data-driven modeling
  • composite materials and structures
  • machine learning
  • reinforced concrete
  • optimization
  • composites in armor design
  • thin-walled structures
  • lightweight structures
  • artificial intelligence
  • sustainability

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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24 pages, 3030 KB  
Article
Fire Resistance Prediction in FRP-Strengthened Structural Elements: Application of Advanced Modeling and Data Augmentation Techniques
by Ümit Işıkdağ, Yaren Aydın, Gebrail Bekdaş, Celal Cakiroglu and Zong Woo Geem
Processes 2025, 13(10), 3053; https://doi.org/10.3390/pr13103053 - 24 Sep 2025
Viewed by 518
Abstract
In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, [...] Read more.
In order to ensure the earthquake safety of existing buildings, retrofitting applications come to the fore in terms of being fast and cost-effective. Among these applications, fiber-reinforced polymer (FRP) composites are widely preferred thanks to their advantages such as high strength, corrosion resistance, applicability without changing the cross-section and easy assembly. This study presents a data augmentation, modeling, and comparison-based approach to predict the fire resistance (FR) of FRP-strengthened reinforced concrete beams. The aim of this study was to explore the role of data augmentation in enhancing prediction accuracy and to find out which augmentation method provides the best prediction performance. The study utilizes an experimental dataset taken from the existing literature. The dataset contains inputs such as varying geometric dimensions and FRP-strengthening levels. Since the original dataset used in the study consisted of 49 rows, the data size was increased using augmentation methods to enhance accuracy in model training. In this study, Gaussian noise, Regression Mixup, SMOGN, Residual-based, Polynomial + Noise, PCA-based, Adversarial-like, Quantile-based, Feature Mixup, and Conditional Sampling data augmentation methods were applied to the original dataset. Using each of them, individual augmented datasets were generated. Each augmented dataset was firstly trained using eXtreme Gradient Boosting (XGBoost) with 10-fold cross-validation. After selecting the best-performing augmentation method (Adversarial-like) based on XGBoost results, the best-performing augmented dataset was later evaluated in HyperNetExplorer, a more advanced NAS tool that can find the best performing hyperparameter optimized ANN for the dataset. ANNs achieving R2 = 0.99, MSE = 22.6 on the holdout set were discovered in this stage. This whole process is unique for the FR prediction of structural elements in terms of the data augmentation and training pipeline introduced in this study. Full article
(This article belongs to the Special Issue Machine Learning Models for Sustainable Composite Materials)
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