Ai-Based Optimized Design of Structural Frames: With Application to Practical Building Designs

This book introduces an auto-design-based optimization for building frames using an artificial neural network (ANN)-based Lagrange method and novel genetic algorithm (GA). The work of great mathematician Joseph-Louis Lagrange and ANNs are merged to identify parameters that optimize structural frames of reinforced concrete, prestressed concrete, and steel frames subject to one or more design constraints. New features for enhancing conventional GA are also demonstrated to optimize structural frames.

New features for optimizing multiple design targets of the building frames are highlighted, while design requirements imposed by codes are automatically satisfied. Chapters provide readers with an understanding of how both ANN-based and novel GA-based structural optimization can be implemented in holistically optimizing designated design targets for building structural frames, guiding readers toward more rational designs that is consistent with American Institute of Steel Construction (AISC) and American Concrete Institute (ACI) standards. ANN-based holistic designs of multi-story frames in general and reinforced concrete, prestressed concrete, and steel frames in particular, are introduced.

This book suits structural engineers, architects, and graduate students in the field of building frame designs and is heavily illustrated with color figures and tables.

Won‐Kee Hong is a professor of architectural engineering at Kyung Hee University, South Korea. He has more than 35 years of professional experience in structural and construction engineering, having worked for Englekirk and Hart, USA; Nihhon Sekkei, Japan; and Samsung Engineering and Construction, Korea. He is the author of Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures and Artificial Neural Network-based Prestressed Concrete and Composite Structures, also published by CRC Press.