Won-Kee Hong

AI-Based Optimized Design of Structural Frames (eBook, PDF)

With Application to Practical Building Designs

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 606
• Erscheinungstermin: 16. Oktober 2024
• Englisch
• ISBN-13: 9781040130964
• Artikelnr.: 72281341

Autorenporträt

Won-Kee 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 AI-based structural designs including building frames. He is the author of several books including Hybrid Composite Precast Systems (Elsevier), Artificial Intelligence-Based Design of Reinforced Concrete Structures (Elsevier), Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures (CRC Press, Taylor & Francis Group) and Artificial Neural Network-based Prestressed Concrete and Composite Structures (CRC Press, Taylor & Francis Group).

Inhaltsangabe

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.