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Buildings
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Advances in Cementitious Materials
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Advances in Cementitious Materials

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 527

Special Issue Editor

Dr. Yang Lu

E-Mail Website
Guest Editor
Civil Engineering, Boise State University, Boise, ID 83725, USA
Interests: computational multiscale/multiphysics modeling of metamaterials; service life prediction of sustainable & durable cementitious materials; characterization, processing, designing and sensing of infrastructure materials; energy efficient building systems and data analytics-enabled smart buildings; critical infrastructure cybersecurity and resilience

Special Issue Information

Dear Colleagues,

Cementitious materials are fundamental to modern infrastructure, with applications in construction, transportation, and other industrial sectors; however, the industry faces challenges such as sustainability demands, performance enhancement, and the need for innovative materials tailored to evolving design and functional requirements.

This Special Issue aims to highlight cutting-edge research and practical advancements in cementitious materials, offering solutions to challenges in durability, sustainability, and adaptability. It invites contributions that advance the field by exploring novel formulations, characterizations, and modeling approaches, with an emphasis on sustainability and resilience. The Special Issue also seeks to bridge the gap between laboratory-scale developments and large-scale field applications, fostering collaboration between academia and industry.

LIST OF TOPICS

The topics of interest for this Special Issue include, but are not limited to:

  • Innovative Cementitious Materials: high-performance concretes, self-healing and multifunctional materials, and nanotechnology applications;
  • Sustainability in Cementitious Materials: alternative binders, recycled aggregates, and carbon footprint reduction strategies;
  • Durability and Long-Term Performance: microstructure–property relationships, resistance to degradation mechanisms, and improved durability in extreme environments;
  • Additive Manufacturing (3D Printing): advances in mix designs, reinforcement strategies, and their application in construction;
  • Advanced Characterization and Testing: non-destructive evaluation methods, multi-scale modeling, and novel experimental techniques;
  • Digital and AI-Powered Solutions: AI for material design, predictive modeling, and digital twins for performance monitoring;
  • Hybrid Systems and Composite Materials: bio-cement, living materials, and multifunctional composites for enhanced properties;
  • Performance in Field Applications: case studies and real-world evaluations of innovative cementitious solutions;
  • Life-Cycle Assessment and Circular Economy: environmental impact assessments, recycling, and sustainable practices.

Dr. Yang Lu
Guest Editor

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. Buildings 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

  • sustainable and resilient cementitious materials
  • advancing performance
  • enhanced durability
  • low-carbon solutions

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

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Research

16 pages, 3862 KiB  
Article
A Navier–Stokes-Informed Neural Network for Simulating the Flow Behavior of Flowable Cement Paste in 3D Concrete Printing
by Tianjie Zhang, Donglei Wang and Yang Lu
Buildings 2025, 15(2), 275; https://doi.org/10.3390/buildings15020275 - 18 Jan 2025
Viewed by 465
Abstract
In this work, we propose a Navier–Stokes-Informed Neural Network (NSINN) as a surrogate approach to predict the localized flow behavior of cementitious materials for advancing 3D additive construction technology to gain fundamental insights into multiscale mechanisms of cement paste rheology. NS equations are [...] Read more.
In this work, we propose a Navier–Stokes-Informed Neural Network (NSINN) as a surrogate approach to predict the localized flow behavior of cementitious materials for advancing 3D additive construction technology to gain fundamental insights into multiscale mechanisms of cement paste rheology. NS equations are embedded into the NSINN to interpret the flow pattern in the 3D printing barrel. The results show that the presented NSINN has a higher accuracy compared to a traditional artificial neural network (ANN) as the Mean Square Errors (MSEs) of the u, v, and p predicted by NSINN are 1.25×10−4, 1.85×10−5, and 3.91×10−3, respectively. Compared to the ANN, the MSE of the predictions are 5.88×10−2, 4.17×10−3, and 1.72×10−2, respectively. Moreover, the mean prediction time used in the NSINN, the ANN, and Computational Fluid Dynamics (CFD) are 0.039 s, 0.014 s, and 3.37 s, respectively. That means the method is more computationally efficient at performing simulations compared to CFD which is mesh-based. The NSINN is also utilized in studying the relationship between geometry and extrudability. The ratio (R = 0.25, 0.5, and 0.75) between the diameter of the outlet and that of the domain is studied. It shows that a larger ratio (R = 0.75) can lead to better extrudability of the 3D concrete printing (3DCP). Full article
(This article belongs to the Special Issue Advances in Cementitious Materials)
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