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Application of Green Composite Concrete Materials in Sustainable Construction Industry
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Application of Green Composite Concrete Materials in Sustainable Construction Industry

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Materials".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 2204

Special Issue Editors

Dr. Wonseok Chung

E-Mail Website
Guest Editor
Department of Civil Engineering, Kyung Hee University, 1732 Deokyoung-Daero, Giheung-gu, Yongin-si 17104, Republic of Korea
Interests: carbon sequestration in construction industry; carbon capture and utilization technology; multi-functional construction materials; composite materials; nano cementitious composite
Special Issues, Collections and Topics in MDPI journals
Dr. Heeyoung Lee

E-Mail Website
Guest Editor
Advanced Concrete Structure Laboratory, Chosun University, Gwangju 61453, Republic of Korea
Interests: cementitious composite; carbon nanomaterials; carbon dioxide capture utilization and storage (CCUS); construction materials; concrete strengthening; rehabilitation of concrete structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, carbon reduction has become an issue due to global climate change. For the sustainable construction industry, research on green composite concrete materials using carbon capture and utilization technology is being actively conducted. In this regard, this Special Issue invites original research articles dealing with experimental and theoretical studies of green composite concrete materials that contribute to our understanding of their performances.

The goal of this Special Issue is to disseminate original research and review studies that address the (experimental or theoretical) advances, trends, challenges, and future perspectives regarding the development, mixture, characterization, and use of green composite concrete materials. The following are some of the topics proposed for this Special Issue:

  • The dispersion, mixture, and hydration of green composite concrete materials;
  • The enhancement of mechanical properties of green composite concrete materials;
  • Multi-functional application (sensing, heating, and curing);
  • The effect of green composite concrete materials on durability and long-term performance;
  • CO2 valorization, sequestration, and utilization by green composite concrete materials;
  • Developing new efficient and sustainable construction materials using carbon-neutral technology;
  • Innovative applications for the construction industry.

We look forward to reading your valuable results.

Dr. Wonseok Chung
Dr. Heeyoung Lee
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. Sustainability 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

  • carbon sequestration
  • CCU (carbon capture and utilization)
  • deep learning, machine learning
  • multi-functional materials
  • composite materials
  • green construction

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Published Papers (2 papers)

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Research

20 pages, 3406 KiB  
Article
Evaluation of Healing in Concretes with Chemical and Bacterial Solutions Exposed to Aggressive Chloride and Carbon Dioxide-Rich Environments
by Fernanda Pacheco, Hinoel Zamis Ehrenbring, Roberto Christ, Rodrigo Périco de Souza, Regina Celia Espinosa Modolo, Victor Hugo Valiatio, Bernardo Fonseca Tutikian and Zemei Wu
Sustainability 2024, 16(24), 10829; https://doi.org/10.3390/su162410829 - 11 Dec 2024
Cited by 1 | Viewed by 1048
Abstract
This paper aimed to evaluate two self-healing mechanisms of concrete exposed to chloride ions and carbon dioxide environments using chemical and bacterial solutions, contributing to understanding the real scenarios of concrete structures application. Expanded perlite (EP) impregnated with chemical and bacterial solutions with [...] Read more.
This paper aimed to evaluate two self-healing mechanisms of concrete exposed to chloride ions and carbon dioxide environments using chemical and bacterial solutions, contributing to understanding the real scenarios of concrete structures application. Expanded perlite (EP) impregnated with chemical and bacterial solutions with the aid of either a vacuum chamber or immersion was used in partial substitution of fine natural aggregate in ratios of 10%, 20%, and 30%. Samples were characterized by a compression strength test. Healing efficiency was evaluated with high precision in stereo zoom microscopy. Further characterization of the samples was obtained from SEM/EDS, and mineral content was determined from XRD. Samples impregnated with a chemical solution formed healing products identified as C-S-H, CaCO3, and SiO2 across and overflowing the fissure. Samples impregnated with the bacterial solution presented a maximum continuous healing region of 1.67 mm and an average of 0.514 mm. A comparison of submersed and wet curing yielded an equal number of results between the techniques. Overall, the products formed were mostly calcite (CaCO3) and C-S-H, while the presence of CO2 and Cl− corrosives did not affect healing, with concentrations of 5% and 3%, respectively. Full article
(This article belongs to the Special Issue Application of Green Composite Concrete Materials in Sustainable Construction Industry)
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14 pages, 3144 KiB  
Article
Predicting Self-Heating Temperature and Influencing Factors in the Cement Composite Mixed with Multi-Walled Carbon Nanotubes Using Machine Learning
by Jaewon Lee, Hyojeong Yun, Yoonseon Cha and Wonseok Chung
Sustainability 2024, 16(23), 10420; https://doi.org/10.3390/su162310420 - 28 Nov 2024
Cited by 1 | Viewed by 715
Abstract
The self-heating temperature of the cement composite mixed with multi-walled carbon nanotubes (MWCNT–cement composite) is influenced by several factors, including the concentration of nano-material. However, conducting experiments to measure this temperature is time-consuming and expensive. Additionally, there are challenges in elucidating the correlations [...] Read more.
The self-heating temperature of the cement composite mixed with multi-walled carbon nanotubes (MWCNT–cement composite) is influenced by several factors, including the concentration of nano-material. However, conducting experiments to measure this temperature is time-consuming and expensive. Additionally, there are challenges in elucidating the correlations between the various influencing factors of the MWCNT–cement composite and its self-heating temperature. This study utilizes machine learning (ML) to predict the self-heating temperature of the MWCNT–cement composite and identify the correlation with influencing factors. ML techniques, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), were employed. These ML models were optimized through hyperparameter tuning and k-fold cross-validation. The predictive performance of each model was evaluated using R2, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) metrics. All ML models exhibited high predictive performance, with the GBM model demonstrating the best thermal prediction capability, achieving an R2 value of 0.9795. Subsequently, the GBM model was used to analyze the major factors affecting the self-heating temperature of the MWCNT–cement composite. The analysis revealed that the concentration of MWCNTs, the amount of voltage, and the outdoor temperature are significant factors determining the self-heating temperature. Furthermore, it was found that the self-heating temperature of the MWCNT–cement composite increases as the concentration of MWCNTs and the amount of voltage increase and as the distance of the mesh decreases. Full article
(This article belongs to the Special Issue Application of Green Composite Concrete Materials in Sustainable Construction Industry)
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