Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures

Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets.

• Uniquely applies the new powerful tools of AI to concrete structural design and optimization
• Multi-objective functions of concrete structures optimized either separately or simultaneously
• Design requirements imposed by codes are automatically satisfied by constraining conditions
• Heavily illustrated in color with practical design examples

The book suits undergraduate and graduate students who have an understanding of collegelevel calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures.

Won‐Kee Hong is a professor of architectural engineering at Kyung Hee University, South Korea. He received his master's and PhD degrees from UCLA, and has worked for Englekirk and Hart, Inc. (USA), Nihhon Sekkei (Japan), and the Samsung Engineering and Construction Company (Korea). Dr. Hong has more than 35 years of professional experience in structural and construction engineering. He has been both an inventor and researcher in the field of modularized composite structures and is the author of more than 100 technical papers and over 100 patents in both Korea and The United States.