Research Topic

Advanced Modeling and Experimental Techniques for Sustainable Concrete

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About this Research Topic

The production of concrete is depleting natural resources, such as limestone, clay, river sand, gravel and emitting a large amount of environmental impact substances. Using industrial inorganic and organic waste to replace cement and aggregates in concrete will not only help to reduce the amount of embodied energy and CO2 emissions associated with cement manufacturing but also mitigate the environmental threats associated with industrial waste materials, therefore promoting the sustainability of concrete. In addition, cement mixture workability, concrete performance, and durability can also be improved.

While, fortunately, we have already made a step forward by taking various actions in improving concrete sustainability, it is vital for us to further promote such activities by using advanced modeling and experimental techniques. On one hand, the use of material models, mostly analytical and sometimes numerical, to embrace multi-scale heterogeneity effects in mass and heat reactive transport, as well as mechanical phenomena in concrete, is only now beginning to be explored. Advanced machine learning tools have been widely applied to design traditional concrete mixtures, but few studies on machine-learning-aided mixture design for sustainable concrete can be found in the literature. On the other hand, various characterization methods can be used to understand the underlying physical and chemical phenomena by considering the multi-scale porous and multi-component nature of concrete composites.

The aim of this Research Topic is to publish papers that advance the field of sustainable concrete through the application of diverse modeling and experimental approaches. Proposed methods should obtain new or enhanced insights into cementitious material behavior, preferably calibrated and/or validated with new or already published experimental data. The scope includes, but is not limited to:
• Capabilities of mathematical modeling applied to sustainable concrete;
• Predicting structure–property relationships of sustainable concrete;
• Use of various characterization methods to understand fresh state rheology, early-age hydration, and hardening development;
• Long-term (aging) properties;
• Machine-learning-aided mixture design for sustainable concrete.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The production of concrete is depleting natural resources, such as limestone, clay, river sand, gravel and emitting a large amount of environmental impact substances. Using industrial inorganic and organic waste to replace cement and aggregates in concrete will not only help to reduce the amount of embodied energy and CO2 emissions associated with cement manufacturing but also mitigate the environmental threats associated with industrial waste materials, therefore promoting the sustainability of concrete. In addition, cement mixture workability, concrete performance, and durability can also be improved.

While, fortunately, we have already made a step forward by taking various actions in improving concrete sustainability, it is vital for us to further promote such activities by using advanced modeling and experimental techniques. On one hand, the use of material models, mostly analytical and sometimes numerical, to embrace multi-scale heterogeneity effects in mass and heat reactive transport, as well as mechanical phenomena in concrete, is only now beginning to be explored. Advanced machine learning tools have been widely applied to design traditional concrete mixtures, but few studies on machine-learning-aided mixture design for sustainable concrete can be found in the literature. On the other hand, various characterization methods can be used to understand the underlying physical and chemical phenomena by considering the multi-scale porous and multi-component nature of concrete composites.

The aim of this Research Topic is to publish papers that advance the field of sustainable concrete through the application of diverse modeling and experimental approaches. Proposed methods should obtain new or enhanced insights into cementitious material behavior, preferably calibrated and/or validated with new or already published experimental data. The scope includes, but is not limited to:
• Capabilities of mathematical modeling applied to sustainable concrete;
• Predicting structure–property relationships of sustainable concrete;
• Use of various characterization methods to understand fresh state rheology, early-age hydration, and hardening development;
• Long-term (aging) properties;
• Machine-learning-aided mixture design for sustainable concrete.


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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