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Advanced Concrete and Cementitious Composite Materials

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 522

Special Issue Editors

School of Design and the Built Environment, Curtin University, Perth, WA 6102, Australia
Interests: artificial intelligence; cementitious material using 3D printing technology; functional building materials
Special Issues, Collections and Topics in MDPI journals
School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: cementitious composites; nanocellulose; hydration kinetics; concrete durability; additive manufacturing
College of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
Interests: engineered cementitious composites; structural engineering; 3D printing concrete; fiber-reinforced concrete; recycled concrete; construction and building materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cement and concrete are fundamental materials in contemporary construction. Currently, traditional cement and concrete materials are facing several significant challenges. These include limitations in durability, crack resistance, and erosion resistance. Additionally, the carbon dioxide emissions produced during cement manufacturing pose considerable environmental concerns. Moreover, modern society has placed higher demands on the multifunctionality of building materials, such as self-sensing, self-healing, and energy efficiency.

In response to these challenges, research has focused on developing advanced cement and concrete composite materials. Efforts have led to the creation of more sustainable and high-performance building materials by introducing binders, fiber-reinforcement technologies, nanomaterials, or smart materials. These materials can not only significantly enhance the durability and crack resistance of structures but also reduce carbon emissions to achieve sustainable development. Meanwhile, the multifunctionality of these materials contributes to reducing maintenance costs and improving the safety and reliability of structures.

This Special Issue, titled “Advanced Concrete and Cementitious Composite Materials”, aims to bring together cutting-edge research on innovative materials in the field of cement and concrete composites. We welcome submissions of original research papers and review articles. Topics of interest include, but are not limited to, the following:

  • Green and sustainable cement and concrete;
  • Geopolymers and alkali-activated binders;
  • Rheology, mechanical performance, and durability;
  • Modeling of cement and concrete composites;
  • Composite mechanics and technology;
  • Design, fabrication, and applications;
  • Self-healing concrete;
  • Degradation, repair, and long-term performance;
  • Innovative additives and binders;
  • High-performance and low-carbon composites;
  • Fiber-reinforced and nanomodified composites;
  • Advanced manufacturing.

Dr. Junbo Sun
Dr. Bo Huang
Dr. Meiyan Bai
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. 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

  • cementitious composite
  • concrete materials
  • sustainability
  • durability
  • low-carbon

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Published Papers (1 paper)

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Research

27 pages, 23793 KB  
Article
Machine Learning Prediction of the Compressive Bearing Capacity of Concrete-Filled Steel Tubes Using Random Forest
by Weidi Su, Yaofei Cheng, Li Wei, Guangda Zhong, Linxiao Zhou, Fei Liu and Kaizhong Xie
Materials 2026, 19(12), 2511; https://doi.org/10.3390/ma19122511 - 10 Jun 2026
Viewed by 177
Abstract
Concrete-filled steel tube (CFST) members are widely used in long-span and high-rise structures due to their high load-bearing capacity and structural efficiency. Accurate prediction of their compressive bearing capacity is essential for reliable design. In this study, a data-driven prediction model based on [...] Read more.
Concrete-filled steel tube (CFST) members are widely used in long-span and high-rise structures due to their high load-bearing capacity and structural efficiency. Accurate prediction of their compressive bearing capacity is essential for reliable design. In this study, a data-driven prediction model based on the Random Forest (RF) algorithm was developed using a database of 154 axial compression tests. A total of 24 parameters, including geometric dimensions, material properties, and sectional characteristics, were considered as input variables, and the model was optimized through five-fold cross-validation and hyperparameter tuning. The results indicate that the proposed model achieves high accuracy and stability, with mean predicted-to-experimental ratios of 1.002 and 0.989 for the training and testing sets, respectively, and maximum deviations within 15%. Compared with existing design codes and alternative machine learning methods, the RF model improves prediction accuracy by approximately 9% and exhibits strong generalization capability. Furthermore, independent experimental validation using nine CFST column tests confirms its reliability, with prediction errors within 5%. These findings demonstrate that the proposed model provides an effective and practical tool for predicting the compressive bearing capacity of CFST members in engineering applications. Full article
(This article belongs to the Special Issue Advanced Concrete and Cementitious Composite Materials)
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