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
Special Issue Editors
Interests: machine learning; composite materials; optimization
Interests: optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
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 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. 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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