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Advanced Cementitious Materials: Integrating Nanotechnology, Sustainability, and Intelligent Design
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Advanced Cementitious Materials: Integrating Nanotechnology, Sustainability, and Intelligent Design

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 December 2025 | Viewed by 164

Special Issue Editors

Dr. Chuang He

E-Mail Website
Guest Editor
School of Architectural Engineering, Taizhou University, Taizhou 318000, China
Interests: concrete corrosion protection and the enhancement of cement-based materials using carbon nanomaterials
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaolei Lu

E-Mail Website
Guest Editor
Key Laboratory of Building Materials Preparation and Testing Technology, University of Jinan, Jinan 250022, China
Interests: special cement; utilization of solid waste; admixtures
Dr. Zhenkun Li

E-Mail Website
Guest Editor
Department of Civil Engineering, Aalto University, 02150 Espoo, Finland
Interests: monitoring of material deterioration; sustainability in maintenance; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights the cutting-edge research that is driving advancements in cementitious materials for enhanced performance, durability, and environmental sustainability. We seek high-quality contributions on the innovative design, characterization, and performance of next-generation cement-based systems.

Topics include nano-engineered composites, carbon nanomaterials (e.g., graphene oxide and SiO2) for improved mechanics/durability, and advanced admixtures that control hydration kinetics. Research on hydration modeling, thermal behavior, microstructure evolution, and long-term performance in harsh environments is also relevant.

Emphasizing sustainability, we particularly encourage submissions on low-carbon/alternative binders, recycled materials, energy-efficient processing, and lifecycle assessment. Additionally, we invite studies utilizing machine learning, artificial intelligence, and multiscale modeling for the prediction, optimization, and intelligent design of cementitious systems.

Interdisciplinary works bridging materials science, civil engineering, environmental science, and data-driven technologies are especially welcome. Both experimental and computational studies are invited, including theoretical frameworks, practical applications, and industrial case studies.

This Special Issue aims to foster collaboration, showcasing novel methodologies for the future of sustainable cement and concrete technology.

Dr. Chuang He
Dr. Xiaolei Lu
Dr. Zhenkun Li
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. 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

  • nanoengineered cementitious materials
  • low-carbon binders
  • machine learning in concrete design
  • hydration kinetics and modeling
  • sustainable construction materials

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Published Papers (1 paper)

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Research

28 pages, 5172 KiB  
Article
Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO2 Emissions Trade-Offs
by Yuzhuo Zhang, Jiale Peng, Zi Wang, Meng Xi, Jinlong Liu and Lei Xu
Buildings 2025, 15(15), 2640; https://doi.org/10.3390/buildings15152640 (registering DOI) - 26 Jul 2025
Abstract
Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this [...] Read more.
Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance economic efficiency, environmental sustainability, and mechanical performance. To address this gap, this study proposes an AI-assisted framework integrating machine learning (ML) and Multi-Objective Optimization (MOO) to achieve a sustainable GPC design. A robust database of 1154 experimental records was developed, focusing on five key predictors: cement content, water-to-binder ratio, aggregate composition, glass powder content, and curing age. Seven ML models were optimized via Bayesian tuning, with the Ensemble Tree model achieving superior accuracy (R2 = 0.959 on test data). SHapley Additive exPlanations (SHAP) analysis further elucidated the contribution mechanisms and underlying interactions of material components on GPC compressive strength. Subsequently, a MOO framework minimized unit cost and CO2 emissions while meeting compressive strength targets (15–70 MPa), solved using the NSGA-II algorithm for Pareto solutions and TOPSIS for decision-making. The Pareto-optimal solutions provide actionable guidelines for engineers to align GPC design with circular economy principles and low-carbon policies. This work advances sustainable construction practices by bridging AI-driven innovation with building materials, directly supporting global goals for waste valorization and carbon neutrality. Full article
(This article belongs to the Special Issue Advanced Cementitious Materials: Integrating Nanotechnology, Sustainability, and Intelligent Design)
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