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Special Issue "Recycling and Predictive Modelling of Green Building Materials"

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".

Deadline for manuscript submissions: 10 January 2024 | Viewed by 1183

Special Issue Editors

Department of Civil Engineering, School of Engineering, Nazarbayev University, Bldg. 3, Room 3.330, 53 Kabanbay Batyr Ave., Nur-Sultan 010000, Kazakhstan
Interests: sustainable cement based composites; structural functional integrated concrete; thermal energy storage concrete; post-elevated temperature performance of cementitious composites; structural health monitoring; self-sensing concrete; self-healing concrete; 3D concrete printing; energy efficient buildings
Special Issues, Collections and Topics in MDPI journals
NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), H/12 Campus, Islamabad 44000, Pakistan
Interests: sustainable construction; self-healing concrete; self-sensing concrete; nano-modified concrete; multi-functional concrete; construction and demolition waste; energy-efficient buildings; lightweight panels; smart bricks
Special Issues, Collections and Topics in MDPI journals
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca Degli Abruzzi, 24, 10129 Turin, TO, Italy
Interests: sustainable concrete; nano-modified concrete; lightweight foamed concrete; 3D concrete printring; functionally graded concrete; construction and demolition waste; self-healing concrete
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many experimental approaches are known to reduce the harmful impacts of the construction industry on the environment. For instance, one can replace the natural coarse aggregate in concrete with recycled concrete aggregate, oil palm shell aggregate, lightweight aggregate, rubber, and other potential waste materials. Replacing natural sand with sugarcane bagasse ash, rice husk ash, eggshell ash, and other different types of industrial and agricultural wastes is also trending in research. In addition, because cement is a carbon-intensive element, we need sustainable alternatives (e.g., the use of natural pozzolanic materials and industrial and agricultural waste as partial replacement of cement) to reduce the associated environmental challenges. The use of waste in the construction industry provides sustainability to the projects in two ways. First, it reduces the amount of cement used, thereby reducing the carbon footprint. Secondly, it helps in the disposal of waste. However, the behavior of waste in cementitious composites is inconsistent due to numerous factors, i.e., concrete mix design, amount of waste used, chemical and physical properties of selected waste, etc. Therefore, the use of wastes in cementitious composites to be used in mega projects requires prior experimental testing. However, the presence of reliable, trustworthy models and formulas to relate the properties of concrete with its ingredients may make it easier for construction engineers to use waste materials in their projects. In this regard, the use of modern computing techniques such as artificial intelligence algorithms can be employed to achieve this objective.

The accurate prediction of the mechanical and durability properties of cementitious composites is a concern since these properties are often required by design codes. The emergence of new sustainable cementitious composites and applications has motivated researchers to pursue reliable models for predicting the mechanical and durability properties of sustainable cementitious composites. Empirical and statistical models, such as linear and nonlinear regression, have been widely used. However, these models require laborious experimental work to develop and can provide inaccurate results when the relationships between concrete properties and mixture composition, and curing conditions are complex. In an era of digitalization, it is obvious that new innovative approaches to model the mechanical, physical, and durability properties of sustainable cementitious composites in the context of building material will be possible through artificial intelligence. The current scientific literature reflects the advancements of such approaches for different fields of engineering, yet this advancement is still in its infancy. We would, therefore, like to invite you to contribute to the state of the science by submitting your manuscript to this Special Issue. The focus lies on applications of machine learning and data-driven methods to model sustainable cementitious composites.

Papers may draw on thematic areas and related subject areas, which may include, but are not limited to, the following areas:

  • Machine learning and deep learning approaches for cement-based composites.
  • Optimization techniques for cement-based composites.
  • Novel algorithms for predicting the mechanical and durability properties of cement-based composites.
  • Recycling of agro-industrial waste in cement-based composites.
  • Automatic defect detection and classification.
  • Non-destructive methods combined with machine learning for sustainable concrete materials.
  • Cementitious composites.
  • Recycled materials.
  • Hybrid binders.
  • Eco-efficient materials.
  • Waste management/recycling.
  • Life cycle analysis.
  • Alternative cementitious materials.
  • Resource efficiency.
  • Recycling and reusability.
  • Green building materials.
  • Circular economy.

Dr. Shazim Memon
Dr. Arsalan Khushnood
Dr. Luciana Restuccia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Materials 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.


  • artificial intelligence
  • machine learning
  • sustainable materials
  • recycled materials
  • green materials
  • 3D printing
  • waste recycling
  • CO2 consumption

Published Papers (1 paper)

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Perspective on the Application of Machine Learning Algorithms for Flow Parameter Estimation in Recycled Concrete Aggregate
Materials 2023, 16(4), 1500; - 10 Feb 2023
Cited by 2 | Viewed by 723
The constantly expanding civilization and construction industry pose new challenges for a sustainable development economy. Aiming to protect the environment is often associated with waste management, thereby reducing the number of landfills. The management of recycled concrete aggregate (RCA) from building demolition and [...] Read more.
The constantly expanding civilization and construction industry pose new challenges for a sustainable development economy. Aiming to protect the environment is often associated with waste management, thereby reducing the number of landfills. The management of recycled concrete aggregate (RCA) from building demolition and its reuse in construction perfectly fits into this trend. The characteristics of post-industrial and recycled materials are not homogeneous as is usually the case with natural materials. This leads to a search for solutions to determine the parameters in the simplest possible manner and with as few resources as possible, while eliminating estimation risks. This task can be solved using machine learning, whose algorithms are increasingly used and developed in many areas of life and industry. The research in this study is aimed at comparing the effectiveness of k-Nearest Neighbors (k-NN) and Artificial Neural Network (ANN) algorithms in determining the permeability coefficient to a linear regression model. This parameter has an important role from the perspective of the application of RCA in civil engineering, particularly in earth construction. Two different RCA materials with different origins and properties were used in the study. The filtration test for each sample was pre-prepared using different compaction energies of 0.17 and 0.59 J/cm3 and for loosely packed samples. Differences in the structures of the test results are presented for both materials. The lowest prediction errors were obtained for the k-NN model. This algorithm obtained for the training sample a coefficient of determination (R2) equal to 0.947 and for the test sample an R2 equal to 0.980. In the case of ANN, the coefficient of determination was in the range of 0.877–0.936. An important part of the study was the interpretation with SHAP of the obtained models, allowing insight into which parameters influenced the predictions. That is significant and novel, considering the heterogeneity of the materials studied, and provides a rationale for further research in this area. Full article
(This article belongs to the Special Issue Recycling and Predictive Modelling of Green Building Materials)
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