Low-Carbon Concrete Solutions for Sustainable Construction: Trends and Perspectives

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

Deadline for manuscript submissions: 10 August 2026 | Viewed by 2211

Special Issue Editors

School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: low-carbon concrete; carbon capture; UHPC; cement chemistry; molecular dynamics simulation

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Guest Editor
School of Civil and Transportation Engineering, Northeast Forestry University, Harbin 150040, China
Interests: low-carbon concrete; UHPC; composite structure; bamboo scrimber; long-term durability
School of Civil Engineering and Architecture, Jiangsu University of Science and Technology, Zhenjiang 212100, China
Interests: magnesium oxysulfate cement; CFD; carbon capture; 3D concrete printing; UHPC
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Special Issue Information

Dear Colleagues,

The construction industry is a major contributor to global carbon emissions, with traditional concrete production accounting for a significant share of energy consumption and CO2 output. In response to urgent global environmental challenges—such as climate change, the depletion of natural resources, and the growing demand for sustainable infrastructure—innovative approaches to concrete production and application are gaining increased attention.

Low-carbon concrete technologies are at the forefront of the transition to more sustainable construction practices. This includes a broad spectrum of innovations, from the utilization of industrial by-products and recycled materials to the development of carbon capture and storage methods, low-clinker or clinker-free cements, nanomaterial integration, and advanced material design through AI and machine learning. These technologies aim to reduce greenhouse gas emissions not only during concrete manufacturing but also throughout its service life, without compromising structural performance and durability.

This Special Issue aims to showcase the latest research trends, breakthroughs, and prospects in the field of low-carbon concrete. We welcome interdisciplinary studies that bridge the gap between material science, structural engineering, environmental performance evaluation, and lifecycle analysis. Submissions may include, but are not limited to, the following:

  • Novel low-carbon binders and supplementary cementitious materials (SCMs);
  • CO2 mineralization and carbon capture in concrete;
  • Recycled aggregates and circular economy approaches;
  • Life-cycle assessment and environmental impact modeling;
  • Machine learning and data-driven concrete mix design;
  • Durability and mechanical performance of green concrete;
  • Design and implementation of low-carbon concrete in real-world infrastructure;
  • Policy, standards, and evaluation frameworks for low-carbon concrete technologies.

The goal of this Special Issue is to bring together cutting-edge research that addresses both the environmental impacts and performance requirements of concrete, encouraging collaboration between academia and industry. We particularly encourage submissions that highlight emerging opportunities, future challenges, and the practical applications of low-carbon concrete technologies in the broader context of sustainable development.

We look forward to your contributions.

Dr. Ming Sun
Dr. Youzhu Lin
Dr. Qiyan Li
Guest Editors

Manuscript Submission Information

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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

  • low-carbon concrete
  • sustainable construction materials
  • supplementary cementitious materials (SCMS)
  • carbon footprint reduction
  • recycled aggregates
  • CO2 mineralization
  • green concrete technology
  • life cycle assessment (LCA)
  • carbon capture and utilization (CCU)
  • machine learning in concrete design

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Published Papers (2 papers)

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Research

25 pages, 3498 KB  
Article
Freeze–Thaw Durability Enhancement of Cement Mortar Incorporating Milled RHA: Hydration Kinetics, Microstructural Refinement, and Strength Evolution
by Shuo Wang and Ming Sun
Buildings 2026, 16(3), 468; https://doi.org/10.3390/buildings16030468 - 23 Jan 2026
Viewed by 559
Abstract
Large quantities of agricultural waste, particularly rice husk ash (RHA), are generated worldwide each year, and the lack of rational, value-added disposal pathways poses both environmental and resource-utilization challenges. To address this practical problem while improving the freeze–thaw (F–T) durability of cement-based materials [...] Read more.
Large quantities of agricultural waste, particularly rice husk ash (RHA), are generated worldwide each year, and the lack of rational, value-added disposal pathways poses both environmental and resource-utilization challenges. To address this practical problem while improving the freeze–thaw (F–T) durability of cement-based materials in cold regions, this study investigates the effects of replacing silica fume (SF) with finely milled RHA on the hydration behavior, mechanical performance, and durability of cement mortar. From a scientific perspective, the freeze–thaw behavior of RHA-modified cementitious materials and the underlying relationships among hydration kinetics, microstructural evolution, and durability remain insufficiently understood. Mortars with different RHA–SF blending ratios were prepared at a constant water-to-binder ratio. Compressive strength was measured before and after F–T cycling, and the underlying mechanisms were investigated using isothermal calorimetry, water absorption tests, and scanning electron microscopy. Results show that SF significantly enhances pre-F–T compressive strength, with the SF-only mixture reaching 56.8 MPa at 28 d, approximately 28.7% higher than the control. With increasing RHA replacement, pre-F–T strength decreased with a non-monotonic variation (40.1–51.5 MPa). F–T cycling caused severe degradation in the reference mortar, with a strength loss rate of 31.75%, whereas RHA- or SF-modified mortars exhibited substantially lower loss rates (6.30–21.54%). Notably, high-RHA mixtures retained residual strengths of 36.0–38.3 MPa after F–T cycling. Although RHA delayed early hydration and increased water absorption, freeze–thaw resistance was not proportionally reduced. These results demonstrate that freeze–thaw durability is governed primarily by long-term microstructural stability rather than early-age strength, and they provide mechanistic evidence supporting the rational utilization of finely milled RHA as a low-carbon supplementary cementitious material for cold-region applications. Full article
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39 pages, 7883 KB  
Article
Hybrid Deep Learning with Conformal Prediction for Recycled Aggregate Self-Compacting Concrete Strength Prediction
by Shuwei Dong and Zhiqin Zhang
Buildings 2025, 15(24), 4419; https://doi.org/10.3390/buildings15244419 - 7 Dec 2025
Cited by 1 | Viewed by 927
Abstract
This study presents a novel hybrid deep learning framework integrating Feature Tokenizer-Transformer (FT-Transformer) with Masked Multi-Layer Perceptron (Masked MLP) for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). The framework addresses incomplete data challenges through a missingness-aware fusion strategy and two-stage [...] Read more.
This study presents a novel hybrid deep learning framework integrating Feature Tokenizer-Transformer (FT-Transformer) with Masked Multi-Layer Perceptron (Masked MLP) for predicting the compressive strength of recycled aggregate self-compacting concrete (RASCC). The framework addresses incomplete data challenges through a missingness-aware fusion strategy and two-stage stacking scheme with Ridge regression. Using a dataset of 289 experimental records with 18 input parameters, the hybrid model achieved robust predictive performance with enhanced generalization stability (Test R2 = 0.940, RMSE = 4.219 MPa) while demonstrating consistent predictions under data missingness conditions up to 25%. SHAP analysis revealed that cement content, water-to-binder ratio, and curing age are the dominant factors influencing RASCC strength. The proposed uncertainty quantification via split conformal prediction provides 90% coverage with average interval width of 8.32 MPa, enabling practical engineering applications with quantified reliability. Full article
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