BUILDING ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTING THE STRUCTURAL DURABILITY OF CONCRETE STRUCTURES, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

The one-day seminar organized by the Institution of Engineers (India) along with the Department of CSE on "Building Artificial Intelligence Models for Predicting the Structural Durability of Concrete Structures" provided an excellent platform for professionals and researchers to explore the potential of AI in enhancing the durability assessment of concrete structures. The event disseminated knowledge, showcased research advancements, and fostered participant collaboration. By leveraging AI techniques, attendees gained valuable insights into predicting the structural durability of concrete structures, enabling informed decision making, optimized maintenance strategies, and improved infrastructure sustainability.
Technical Session - I An expert and researchers in structural engineering and AI Mr Guru Purushoth S delivered presentations during the technical sessions. They shared their research findings, methodologies, and case studies related to building AI models for predicting the structural durability of concrete structures. The sessions gave attendees valuable insights into this domain's latest advancements, challenges, and potential solutions. Participants had the opportunity to learn from experts and gain a deeper understanding of the subject matter. Technical Session - II In Technical Session II Dr Yuvaraj N, HoD/CSE deliberated on AI models for predicting the structural durability of concrete structures utilize machine learning and other AI techniques to assess factors influencing durability. They analyze data on material properties, environmental conditions, and construction practices. The models estimate remaining service life and deterioration potential by incorporating these factors. Key steps include data collection, feature selection, model development, training, validation, and performance evaluation. Challenges include data availability and model interpretability. Future directions involve advanced AI techniques and hybrid models. AI models enhance maintenance strategies, resource allocation, and infrastructure sustainability.