主讲人Speaker:
Konstantinos Daniel Tsavdaridis, Professor, City St George’s, University of London
报告主题Title:
Structural Design Codes with AI: From Safety Margins to Smart Design
主持人 Chair:
章子华 教授 黑料社
Zihua Zhang, Professor, Ningbo University
报告时间Date and Time：
2025年9月12日15:00-16:00
September 12, 2025, 15:00-16:00 (UTC + 8:00)
报告地点Venue：
线下：黑料社 思禹建工A409
线上：Zoom Meeting ID: 859 3473 0325, Password: 515096
报告人简介Biography
Professor Tsavdaridis holds the Chair of Structural Engineering in the Department of Engineering, School of Science & Technology at City St George’s, University of London, Head of Structural Engineering Laboratory, and Director of the 3-D Modular Building Construction (3DMBC) research group. His research focuses on innovative designs for structural members, seismic behavior of highly optimized tall buildings and towers, and the application of 3D printing in construction. In 2019, he was awarded with a Senior Fellowship from the Royal Academy of Engineering to work on digitalization, modular construction and its circular economy aspects. The recent years he is focusing on the application of AI into the design of structures and he is the Founder & Chief Scientist of Efestos Hub – a data company decarbonizing AEC through AI. Professor Tsavdaridis is a Chartered Engineer and Fellow of the Institution of Civil Engineers (CEng FICE) and the Institution of Structural Engineers, and contributes to CEN/TC and BSi committees for the development of structural guidelines and standards in Europe and UK.
内容摘要Abstract
This presentation explores how Artificial Intelligence can transform traditional structural design codes, such as the Eurocodes, by enhancing accuracy, reducing conservative safety factors, and enabling optimized designs. AI, especially physics-informed models, offers the ability to predict and distinct complex failure mechanisms and their combinations that elude simplified mechanical models—particularly in thin-walled steel structures and steel-concrete composite systems where multiple or interconnected failure modes may occur. These insights are vital as the industry shifts toward modern methods of construction, such as modular systems, where adaptability and predictive intelligence are key. AI agents and assistants can act as design co-pilots, streamlining code interpretation and adaptation for project-specific optimizations. Computer vision and pattern recognition are employed to assess the structural components—paving the way for a circular economy in construction. Moreover, AI enhances predictive modelling accuracy and provides real-time assistance in delivering higher quality, code-compliant structures that reflect the real-world behavior of materials and connections more faithfully.