Название: Artificial Neural Network-based Optimized Design of Reinforced Concrete StructuresАвтор: Won-Kee Hong
Издательство: CRC Press
Год: 2023
Страниц: 581
Язык: английский
Формат: pdf (true)
Размер: 52.2 MB
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.
An application to a design of doubly RC beams minimizing cost, environmental impact, and beam weight is conducted based on the five-step optimization to find optimized points (stationary points) of a Lagrange function using the optimization and training toolbox provided by MATLAB. Examples of decision-making using optimized results are also introduced in this chapter that can aid engineers for final design decisions. The algorithm has been initially proposed by Hong and Nguyen, utilizing Artificial Neural Networks (ANN) to generalize objective functions.
• 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 college level calculus and will be especially beneficial to engineers and contractors who seek to optimize concrete structures.
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