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010 a| 2022029378;

020 a| 9781032323688q| (hardback);

020 a| 9781032323695q| (paperback);

020 z| 9781003314684q| (ebook);

035 a| (WaSeSS)ssj0002736682;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| TA683.2b| .H65 2023;

082 00 a| 624.1/83412| 23/eng/20221011;

100 1 a| Hong, Won-Keec| (Professor of architectural engineering)?| UNAUTHORIZED;

245 10 a| Artificial neural network-based optimized design of reinforced concrete structuresh| [electronic resource] /c| Won-Kee Hong.;

260 a| Boca Raton :b| CRC Press,c| 2023;

263 a| 2212;

300 a| pages cm;

504 a| Includes bibliographical references and index.;

505 0 a| Introduction to Lagrange optimization for engineering applications -- AI-based Lagrange optimization adopting universally generalizable functions -- An optimized design of reinforced concrete columns based on an ANN-based Hong-Lagrange method -- Optimizing reinforced concrete beam cost using ANN-based Hong-Lagrange method -- ANN-based structural designs using Lagrange multipliers optimizing multiple objective functions.;

506 a| Available only to authorized users.;

520 a| "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 Hong-Lagrange method. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. The book suits undergraduate and graduate students who have a good understanding of college-level calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

650 0 a| Reinforced concrete constructionx| Mathematical models=| ^A39386;

650 0 a| Structural optimization=| ^A448091;

650 0 a| Lagrangian functions=| ^A63786;

650 0 a| Neural networks (Computer science)=| ^A263002;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9781003314684;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6656998-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/18/2025x| EBOOKz| JERESOURCE;

Artificial neural network-based optimized design of reinforced concrete structures / Won-Kee Hong.

Author/creator	Hong, Won-Kee
Format	Electronic
Publication Info	Boca Raton : CRC Press, 2023
Description	pages cm
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Reinforced concrete construction--Mathematical models. Structural optimization Lagrangian functions Neural networks (Computer science)

Contents	Introduction to Lagrange optimization for engineering applications -- AI-based Lagrange optimization adopting universally generalizable functions -- An optimized design of reinforced concrete columns based on an ANN-based Hong-Lagrange method -- Optimizing reinforced concrete beam cost using ANN-based Hong-Lagrange method -- ANN-based structural designs using Lagrange multipliers optimizing multiple objective functions.
Abstract	"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 Hong-Lagrange method. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. The book suits undergraduate and graduate students who have a good understanding of college-level calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2022029378
ISBN	9781032323688 (hardback)
ISBN	9781032323695 (paperback)
ISBN	(ebook)

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