Machine Learning in Cement-Based Materials: Advances and Applications

Dear Colleagues,

Cement-based material (CBM) constitutes a type of basic building material that is widely used internationally, with a global annual production of about 4.1 billion tons. Taking Australia as an example, approximately AUD 75 billion has been allocated to the development of infrastructure over the next ten years, such as new airports, tunnels, bridges, etc. The application of CBM plays an important role in the construction industry and has become an indispensable substance for economic development.

However, the production of cement is usually energy-intensive. The production of one ton of cement causes approximately 900 kg of CO₂ emissions into the atmosphere. Globally, cement companies emit nearly 2 billion tons of CO₂ during production per year (about 6 to 7% of the planet's total CO₂ emissions). At this rate, the cement industry will emit 3.5 billion tons of CO₂ per year by 2025, which is approximately equal to the current European total emissions (including the transport and energy industries). The massive emissions of CO₂ are significantly affecting global climate change and threatening sustainable human development. Therefore, how to improve the efficiency of cement while maintaining the function of CBM is a key objective for the cement industry.

In recent years, new advanced techniques like machine learning (ML) have been developed and applied effectively to many CBM problems. By embracing ML techniques, more cost-effective CBM designs can be achieved in a timely manner, which is likely to reshape the entire CBM industry. Many new hybrid and advanced AI techniques are being proposed. The development and application of these ML techniques in CBM should be explored with new case studies.

The main objective of the Special Issue is to collect state-of-the-art research findings on the latest developments and challenges in the field of CBM. High-quality original research papers that present theoretical frameworks, methodologies, and the application of case studies from a single- or cross-country perspective are welcome, as well as review articles.

Prof. Dr. Chongchong Qi
Guest Editor

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• cement-based materials
• concrete
• construction industry
• machine learning
• artificial intelligence
• data mining