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

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

Hong, Won-Kee

ウェブストア価格 ¥41,395（本体¥37,632）
CRC Press（2024/10発売）
外貨定価 US$ 200.00
ポイント 376pt

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製本 Hardcover:ハードカバー版／ページ数 582 p.
言語 ENG
商品コード 9781032536811
DDC分類 624.1028563

Full Description

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.

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.