RIMNI Special Issue - Machine Learning–Driven Net-Zero Design and Performance Optimization of Sustainable Cementitious and Concrete Materials

Deadline Date: 30 September 2026

The cement and concrete industry is responsible for a significant share of global CO₂ emissions. It makes the development of low-carbon, high-performance cementitious materials a critical challenge for achieving net-zero targets. This Special Issue focuses on innovative strategies for the formulation, optimization, and performance evaluation of low-CO₂ and green cement-based materials. The particular emphasis will be on machine learning and data-driven approaches.

Topics include but not limited to:

1. The design and optimization of sustainable concrete and cementitious composites incorporating industrial by-products

2. Performance and durability assessment of construction materials

3. Application of machine learning, computer vision, and environment simulation in materials design, carbonation prediction, and durability evaluation.

4. Studies on sustainable assessment frameworks, low-carbon innovation pathways, and circular economy-oriented material utilization.

By integrating advanced computational methods with experimental investigations, this Special Issue aims to promote net-zero optimization of cementitious materials while ensuring mechanical performance, durability, and long-term sustainability. Contributions addressing multi-objective optimization, lifecycle assessment, and digital technologies for green cement and concrete are particularly encouraged. This Special Issue seeks to provide a multidisciplinary platform for researchers and engineers to accelerate the transition toward low-carbon and circular construction materials.

Deadline Date: 30 September 2026

The cement and concrete industry is responsible for a significant share of global CO₂ ... show more

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Dr. Enming Li Email Website

Affiliation: Department of Mining Engineering and Earth Sciences, Universidad Politécnica de Madrid, Madrid, 28040, Spain

Research Interests: prediction of the strength of sustainable concrete, machine learning, prediction of concrete carbonation, computer vision

Dr. Ning Zhang Email Website

Affiliation: School of Mechanics and Construction Engineering, Jinan University, Guangzhou, 510632, China

Research Interests: sustainable assessment, low-carbon innovation, circular economy, environment simulation

Dr. Bin Xi Email Website

Affiliation: Paul Scherrer Institute, orschungsstrasse 111, Villigen, 5232, Switzerland

Research Interests: net-zero optimization of cementitious materials, machine learning combined, low-CO₂/green cement formulation and optimization performance and durability of tailings-based construction materials

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Deadline Date: 30 September 2026

ISSN: ISSN 1886-158X (online version) - ISSN 0213-1315 (printed version)

Licence: Creative Commons BY-NC-SA license

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