This book introduces an auto-design-based optimization for building frames using an artificial neural networks (ANN)-based Lagrange method and genetic algorithms (GAs). 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 GAs 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 GA-based structural optimization can be implemented in optimizing designated design targets for building structural frames, guiding them towards more rational design 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.
The book suits structural engineers, architects, and graduate students in the field, and is heavily illustrated with color figures and tables.
This book introduces an auto-design-based optimization for building frames using an artificial neural networks (ANN)-based Lagrange method and genetic algorithms (GAs), and consistent with codes of practice. It is heavily illustrated with color figures and tables.
1. Introduction to optimizations of structural frames.
2. An auto-design for optimizing RC frames using the ANN-based Hong-Lagrange algorithm.
3. An auto-design for optimizing prestressed frames using the ANN-based Hong-Lagrange algorithm.
4. An auto-design for optimizing steel frames using the ANN-based Hong-Lagrange algorithm.
5. A new GA using mutations with dynamic ranges and a probability-based natural selection method to optimize precast beams.
6. AI-based optimizations of RC and PT frames (AI-FRT) using penalty-based genetic algorithm with probabilistic-based natural selections (PPD-GA) using dynamic mutations.
WonKee Hong is a professor of Architectural Engineering at Kyung Hee University, Republic of Korea. He has more than 35 years of professional experience in structural and construction engineering, having worked for Englekirk and Hart, USA; Nihon Sekkei, Japan; and Samsung Engineering and Construction, Korea. He has published more than 20 international papers in the field of AIbased structural designs including building frames. He is the author of several books including Hybrid Composite Precast Systems (Elsevier), Artificial IntelligenceBased Design of Reinforced Concrete Structures (Elsevier), Artificial Neural Networkbased Optimized Design of Reinforced Concrete Structures (CRC Press, Taylor & Francis Group) and Artificial Neural Networkbased Prestressed Concrete and Composite Structures (CRC Press, Taylor & Francis Group).
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