Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures (Hardcover)

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.

General

Imprint:	Taylor & Francis
Country of origin:	United Kingdom
Release date:	December 2022
First published:	2023
Authors:	Won-Kee Hong
Dimensions:	254 x 178mm (L x W)
Format:	Hardcover
Pages:	564
ISBN-13:	978-1-03-232368-8
Categories:	Books > Professional & Technical > Civil engineering, surveying & building > General Books > Professional & Technical > Environmental engineering & technology > General Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks Promotions
LSN:	1-03-232368-X
Barcode:	9781032323688

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