Predicting Axial Load in Concrete-Filled Steel Tubes delves into advanced predictive techniques for evaluating the axial compression capacity of concrete-filled steel tubes (CFSTs). This course highlights innovative hybrid models combining fuzzy systems with cutting-edge optimization algorithms such as the Firefly Algorithm (FFA) and Differential Evolution (DE). These models outperform traditional design codes, offering improved accuracy and efficiency in predicting structural performance, crucial for enhancing safety and cost-effectiveness in engineering projects. Participants will explore the mechanics of CFST columns, their applications in high-rise buildings and bridges, and the practical implications of improved predictive methodologies.

Key highlights include:

• Understanding the mechanical behaviour of CFSTs under axial loads and their design challenges.
• Learning how hybrid predictive models enhance accuracy compared to traditional methods.
• Applying machine learning and optimization techniques to real-world structural engineering problems.

By the end of this course, attendees will gain practical knowledge and skills to implement advanced predictive methods in structural design, ensuring more robust and reliable outcomes for modern engineering challenges.