Machine Learning Applications for Engineered Geomaterials Development
A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".
Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2806
Special Issue Editors
Interests: civil engineering materials; sustainable development; artificial intelligence; material innovation; geotechnology; environmental geotechnology
Special Issues, Collections and Topics in MDPI journals
Interests: sustainability; construction materials; soil, environment; geotechnical engineering; water supply; sanitation and hygiene (WASH); waste management; infrastructure
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geomaterials are materials that are influenced by geological systems and that have served humankind for multiple centuries. Recent urbanisation and unprecedented usage have put pressure on these materials and has caused rapid depletion. Newly developed multiphase/scale analysis methods should improve our understanding of geomaterial behaviour. If a clear understanding can be achieved, it could greatly benefit the safety and reliability of geotechnical infrastructures built on/with geomaterials. All structural applications now produce huge loads both directly and indirectly, which removes the need for generic geomaterials, and hence, a newer dimension has come into use, which is engineered geomaterials. Engineered geomaterials are used in a wide range of applications including structures under severe environments. The application of AI and ML is steadily growing due to their versatility and application standards. Material design requires many resources in analysing and understanding a material’s behaviour, which is currently widely supported by machine learning applications. The aim of this Special Issue is to understand the recent research ongoing in geomaterial development for the creation of sustainable and resilient infrastructure with a prime focus on machine learning applications.
Prof. Dr. Gobinath Ravindran
Dr. Isaac Akinwumi
Guest Editors
Manuscript Submission Information
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Keywords
- geomaterials
- engineered geomaterials
- artificial intelligence
- machine learning
- characterisation