Performances of Structural Concrete: Data-Driven Analysis Using AI, Numerical and Experimental Investigation
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 71
Special Issue Editors
Interests: numerical modelling; concrete structures; AI applications; data-driven analysis and experimental work
Interests: soft computing techniques; machine learning; green goncrete; recycle material; composite material; plate theories; timber structures
Special Issue Information
Dear Colleagues,
The advent of advanced analytical techniques and artificial intelligence (AI) is revolutionising the field of structural concrete performance analysis. This Special Issue focuses on integrating data-driven methodologies, numerical simulations, and experimental investigations to enhance the understanding and prediction of structural concrete behaviours. Leveraging AI and machine learning algorithms, researchers can now process vast datasets to identify patterns and predict the performance of concrete structures under various conditions with unprecedented accuracy.
Numerical methods, including finite element analysis, complement these data-driven approaches by providing detailed insights into concrete's mechanical properties and failure mechanisms. Experimental investigations remain crucial, offering empirical data to validate and refine computational models and AI predictions. By synergising these approaches, this issue aims to address the complexities of concrete performance, such as durability, strength, and resilience under dynamic loads. Contributions to this issue encompass a wide range of topics, including, but not limited to, AI-based predictive modelling, advancements in numerical techniques, innovative experimental methodologies, and case studies demonstrating practical applications. This multidisciplinary approach enhances the predictive capabilities and reliability of structural concrete analyses and paves the way for developing smarter, more resilient infrastructure. Through this compilation, we seek to foster deeper understanding and inspire innovative solutions in structural concrete performance.
Dr. Rwayda Kh S. Al-Hamd
Dr. Asad S. Albostami
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.
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Keywords
- artificial intelligence
- machine learning
- analytical techniques
- numerical methods
- finite element analysis
- concrete
- experimental investigations
