Machine Learning in Cement-Based Materials: Advances and Applications
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Construction and Building Materials".
Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 1650
Special Issue Editor
Interests: solid waste minimisation; cemented paste backfill; pollution reduction; recycling; first-principles calculations; molecular dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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. 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.
Keywords
- cement-based materials
- concrete
- construction industry
- machine learning
- artificial intelligence
- data mining
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
