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Full Description
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.
Contents
1. Introduction to Lagrange optimization for engineering applications 2. AI-based Lagrange optimization adopting universally generalizable functions 3. An optimized design of reinforced concrete columns based on an ANN-based Hong-Lagrange method 4. Optimizing reinforced concrete beam cost using ANN-based Hong-Lagrange method 5. ANN-based structural designs using Lagrange multipliers optimizing multiple objective functions Appendix A Appendix B Appendix C Appendix D
