Nanotechnology for Advanced and Sustainable Construction Materials

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Synthesis, Interfaces and Nanostructures".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 1539

Special Issue Editors

College of Civil and Transportation Engineering, Hohai University, Nanjing 211100, China
Interests: nano modified concrete; carbon sequestration
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Guest Editor
School of Highway, Chang’an University, Xi’an, China
Interests: road material behavior and engineering resilience; smart road materials and pavements; roadway resource evaluation; environment & traffic monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nanotechnology has changed and will continue to change our vision and expectations of construction materials. Construction materials are an important subject in civil engineering, and important construction materials include concrete, steel, stones, bricks, plastics, timber, glass, and many metals. As nanotechnology has grown, new, sustainable, and smart nanomaterials have been developed by engineers and researchers all over the world and this process continues even today. Construction nanomaterials are widely used across the world due to their higher strength and lower density. For example, nanomaterials such as nano-silica, graphene, nano clays, nano CaCO3, carbon nanotubes, etc., were found to be a very effective additive to polymers and concrete, and these materials significantly improved the workability and strength of polymers and concrete.

This Special Issue presents recent trends in the field of construction engineering with an emphasis on the applications of nanomaterials and their beneficial effects at the nanoscale. This Special Issue is focused on, but not confined to, five main research areas involving the construction of nanomaterials.

  1. Nano silica in construction engineering;
  2. Metal-based nanomaterials in construction engineering;
  3. Concrete/reinforced concrete nanomaterials;
  4. Nanofibers in construction engineering;
  5. Smart nanomaterials (self cleaning; stress sensing; self compacting, building integrated photovoltaics, etc.).

We welcome full papers, communications, and review articles emphasizing the broad scope of the topic.

Dr. Yue Gu
Prof. Dr. Zhuangzhuang Liu
Guest Editors

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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

  • nanotechnology in construction materials
  • nano silica
  • metal-based nanomaterials
  • concrete/reinforced concrete
  • nanofibers
  • smart nanomaterials in construction engineering

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Published Papers (1 paper)

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Research

17 pages, 7450 KiB  
Article
Elucidating Rheological Properties of Cementitious Materials Containing Fly Ash and Nanosilica by Machine Learning
by Ankang Tian, Yue Gu, Zhenhua Wei, Jianxiong Miao, Xiaoyan Liu and Linhua Jiang
Nanomaterials 2024, 14(21), 1700; https://doi.org/10.3390/nano14211700 - 24 Oct 2024
Viewed by 1166
Abstract
Researching the rheology contributes to enhancing the physical and mechanical properties of concrete and promoting material sustainability. Despite the challenges posed by numerous factors influencing viscosity, leveraging machine learning in the era of big data emerges as a viable solution for predicting the [...] Read more.
Researching the rheology contributes to enhancing the physical and mechanical properties of concrete and promoting material sustainability. Despite the challenges posed by numerous factors influencing viscosity, leveraging machine learning in the era of big data emerges as a viable solution for predicting the general properties of construction materials. This study aims to create models to forecast the rheological properties of cementitious materials containing fly ash and nanosilica. Four models—Random Forest, XGBoost, ANN, and RNN (Stacked LSTM)—are employed to predict and assess shear rate versus shear stress and shear rate versus apparent viscosity curves. Through hyperparameter adjustments, RNN (Stacked LSTM) exhibits excellent performance, achieving a coefficient of determination (R2) of 0.9582 and 0.9257 for the two curves, demonstrating superior statistical parameters and fitting effects. The RNN (Stacked LSTM) exhibited a better generalization ability, suggesting it will be more reliable for future prediction in cementitious material viscosity. Full article
(This article belongs to the Special Issue Nanotechnology for Advanced and Sustainable Construction Materials)
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