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Research on Properties of Novel Building Materials
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Research on Properties of Novel Building Materials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 912

Special Issue Editors

Dr. Yue Gu

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: carbon capture and carbon neutral building materials; nano-modified hydraulic and marine engineering materials; building energy storage and bionic multifunctional materials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Liu

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: durability; concrete structure; microstructure
Dr. Kai Lyu

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: durability; concrete structure; microstructure

Special Issue Information

Dear Colleagues,

High speed has given way to high quality in the building industry's development trend in recent years. There is a growing need for environmentally friendly, durable, and multipurpose building materials to satisfy the public's expectations for a comfortable living space. Even building materials are on the verge of undergoing a paradigm shift.

For this Special Issue, authors are kindly invited to submit high-quality papers on the following topics:

  1. CO2 storage in building materials.
  2. Energy storage in concrete.
  3. Self-sensing in building materials.
  4. Artificial intelligence-enhanced building materials.
  5. Nano-modified building materials, such as piezoresistive effect, photocatalytic color change, self-cleaning, and other properties.

Dr. Yue Gu
Prof. Dr. Lin Liu
Dr. Kai Lyu
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. 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

  • construction materials
  • durability
  • low carbon
  • CO2 uptake
  • energy storage
  • self-sensing
  • artificial intelligence
  • nano-modified

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Published Papers (2 papers)

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Research

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27 pages, 20658 KiB  
Article
Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions
by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu and Lauren Y. Gómez-Zamorano
Appl. Sci. 2025, 15(13), 7125; https://doi.org/10.3390/app15137125 - 25 Jun 2025
Viewed by 167
Abstract
This study investigates the quantitative relationship between material composition and the performance of foam concrete based on 170 validated experimental datasets extracted from the existing literature. The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance [...] Read more.
This study investigates the quantitative relationship between material composition and the performance of foam concrete based on 170 validated experimental datasets extracted from the existing literature. The statistical approach combined with machine learning modeling was employed to systematically analyze and predict key performance indicators. Pearson correlation analysis was used to identify the parameters affecting mechanical and thermal properties. The analysis revealed that the water-to-cement ratio (W/C) and cement content were the most influential factors for mechanical properties, while density and the coarse-to-fine aggregate ratio (Cag/Fag) had the greatest impact on thermal conductivity. To overcome the limitations of traditional empirical models in capturing complex nonlinear relationships, a predictive framework with eight machine learning algorithms was established. Among these, Neural Network Regression exhibited the highest accuracy for mechanical property prediction, with a coefficient of determination of R2 = 0.987 for compressive strength and R2 = 0.932 for flexural strength. For thermal conductivity, support vector regression achieved the best predictive performance with R2 = 0.933. Error analysis demonstrated significant differences in prediction accuracy across performance indicators: compressive strength was the easiest to predict, followed by flexural strength, while thermal conductivity was the most challenging. Based on practical engineering requirements, a hierarchical model selection strategy was proposed. Specifically, Neural Network Regression is prioritized for mechanical properties, and support vector regression is prioritized for thermal properties. Decision Tree Regression is recommended as a general-purpose model. The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency. Full article
(This article belongs to the Special Issue Research on Properties of Novel Building Materials)
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Review

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20 pages, 4857 KiB  
Review
Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
by Ruyan Fan, Ankang Tian, Yikun Li, Yue Gu and Zhenhua Wei
Appl. Sci. 2025, 15(9), 4733; https://doi.org/10.3390/app15094733 - 24 Apr 2025
Cited by 1 | Viewed by 543
Abstract
Nano-modified concrete has attracted wide attention due to its improved mechanical properties. Among them, compressive strength is the most critical indicator. However, testing nano-concrete is costly and complex because it requires control over many factors, such as nanoparticle content and dispersion. Machine learning [...] Read more.
Nano-modified concrete has attracted wide attention due to its improved mechanical properties. Among them, compressive strength is the most critical indicator. However, testing nano-concrete is costly and complex because it requires control over many factors, such as nanoparticle content and dispersion. Machine learning offers a data-driven way to predict compressive strength more efficiently. It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. In this article, the development of machine learning research in predicting the strength of concrete enhanced by nanoparticles is reviewed. First, we systematically outline a three-phase ML framework encompassing data curation, model development, and validation protocols; next, popular algorithms and their uses in predicting the strength of nano-modified concrete are evaluated, such as Artificial Neural Networks, K-Nearest Neighbor, Random Forest, etc. Ultimately, the article offers a forward-looking perspective on how future machine learning advancements can foster and accelerate the development of nano-modified concrete. Full article
(This article belongs to the Special Issue Research on Properties of Novel Building Materials)
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