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Synopsis
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.
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About the Author
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.
"About this title" may belong to another edition of this title.
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures (Paperback)
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ISBN 10: 1032323698
ISBN 13: 9781032323695
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Paperback. Condition: new. Paperback. 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 optimizationMulti-objective functions of concrete structures optimized either separately or simultaneouslyDesign requirements imposed by codes are automatically satisfied by constraining conditionsHeavily illustrated in color with practical design examplesThe 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. This book introduces AI-based Lagrange optimization techniques which enable more optimised concrete structural design, while conforming to codes of practice. The principles are outlined and then applied to the design of RC columns and beams, offering insight for both advanced practitioners and graduate students. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781032323695
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures
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Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures
Published by
Taylor & Francis Ltd, 2024
ISBN 10: 1032323698
ISBN 13: 9781032323695
New
Paperback / softback
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