Advancing Construction Material Performance: Integrating Machine Learning Innovations
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".
Deadline for manuscript submissions: closed (10 September 2024) | Viewed by 485
Special Issue Editors
Interests: digital project management; digital transformation; AI in project management; sustainable project management
Special Issues, Collections and Topics in MDPI journals
2. Faculty of Arts and Design, University of Canberra, 11 Kirinari St., Bruce, ACT 2617, Australia
Interests: dynamic structure; construction materials; concrete performance optimization; machine learning in construction; generative AI
Special Issue Information
Dear Colleagues,
The construction industry, pivotal in shaping human development, is undergoing a significant transformation through the integration of machine learning (ML). This Special Issue focuses on the most widely used construction materials, e.g., concrete and timber, which play a crucial role due to their unique combination of strength, affordability, and durability. The use of ML in construction materials science has seen transformative potential, drastically changing how we approach the mixture design, property prediction, performance optimization, and characterization of building materials.
We invite submissions that contribute to the understanding and advancement of ML applications in materials science for construction. We are particularly interested in articles that demonstrate how ML is transforming the design, analysis, and optimization of building materials that are fundamental to modern infrastructures. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Innovative ML techniques for predictive modelling in materials science for construction;
- Case studies on the real-world application of ML in optimizing construction materials;
- The role of ML in enhancing the sustainability and eco-friendliness of constriction materials;
- ML-driven material characterization techniques and their impact on materials science in construction;
- Overcoming challenges in data quality and representativeness in ML-based studies.
We look forward to receiving your contributions.
Dr. Saeed Banihashemi
Dr. Khuong LeNguyen
Dr. Afaq Ahmad
Guest Editors
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. Buildings 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
- building material
- AI
- sustainability
- construction material
- performance prediction and optimization
- machine learning
- concrete and structures
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