Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications

Dear Colleagues,

We are standing at the brink of a technological revolution in structural engineering, in which machine learning (ML) is set to emerge as a pivotal force in reshaping traditional methodologies. This Special Issue, entitled "Emerging Trends in Machine Learning for Structural Engineering: Innovations and Applications", aims to capture and disseminate cutting-edge research on topics where ML technologies intersect with structural engineering to enhance the design, analysis, and sustainability of infrastructure.

As we advance further into the 21st century, the drive for smarter, more resilient, and more sustainable construction grows ever stronger and machine learning offers unprecedented capabilities in this regard, from optimizing the design of complex structures to enabling real-time monitoring and predictive maintenance. This Special Issue seeks to explore these advancements comprehensively, highlighting both theoretical innovations and practical implementations.

We invite submissions of original research, both theoretical and experimental, detailed case studies, and comprehensive review papers. Submissions should demonstrate novel ML applications within the context of structural engineering and contribute significantly to the existing body of knowledge.

Topics of interest include, but are not limited to:

1. Machine Learning: applications in predictive maintenance, automated design, and real-time structural health monitoring;
2. Finite Element Analysis: the use of advanced computational models for ML applications, etc.;
3. Cold-Formed Steel Structures: innovations and ML applications in the design and analysis;
4. Modular Construction: ML-driven optimization of prefabrication and assembly processes;
5. Innovative Concrete Materials and Structures: AI in the development and implementation of new concrete materials;
6. Sustainable Structures and Materials: the integration of ML into the design and management of sustainable construction practices;
7. Risk Assessment and Disaster Mitigation: machine learning models for assessing risks and enhancing the resilience of structures against natural disasters;
8. Data Analytics in Construction Management: using ML to analyze project data for better decision-making and operational efficiency;
9. Automated Compliance Checking: ML algorithms to ensure designs meet regulatory and safety standards;
10. Smart Sensors and IoT: the integration of ML with IoT devices for enhanced monitoring and control systems at construction sites;
11. 3D Printing and Digital Fabrication: ML applications in optimizing 3D printing techniques and processes for building components;
12. Lifecycle Assessment: ML techniques to evaluate the environmental impact of building materials and methods throughout their lifecycle;
13. Fire performance assessment of structures: ML applications in the fire performance assessment of structures.

Prof. Dr. Oladimeji Benedict Olalusi
Dr. Lenganji Simwanda
Dr. Gatheeshgar Perampalam
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 monthly 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.

• machine learning in structural engineering
• predictive maintenance
• real-time structural health monitoring
• finite element analysis
• cold-formed steel structures
• modular construction
• advanced concrete materials
• sustainable construction practices
• disaster risk assessment
• construction data analytics
• automated compliance in engineering
• smart sensors and iot in construction
• 3D printing in construction
• lifecycle assessment of building materials
• fire performance modeling