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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 413

Special Issue Editors

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

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 (1 paper)

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Review

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
Viewed by 277
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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