AI-based technologies and, in a broader sense, digital technologies have become very important in civil engineering design.
Artificial Intelligence-based Design of Reinforced Concrete Structures: Artificial Neural Networks for Engineering Applications is an essential reference resource for those readers who want to learn how to perform artificial intelligence-based structural design.
The book describes in detail the main concepts of ANNs and their application and use in civil and architectural engineering. It shows how neural networks can be established and implemented depending on the nature of a broad range of diverse engineering problems.
The design examples include both civil and architectural engineering solutions, for both structural engineering and concrete structures.
Those who have not had the opportunity to study or implement neural networks before will find this book very easy to follow. It covers the basic network theory and how to formulate and apply neural networks to real-world problems. There are plenty of examples based on real engineering problems and solutions.
- Civil engineers will learn the fundamentals of AI and ANNs and how to apply them in simple reinforced concrete design cases
- Contains practical case study examples on the application of AI technology in structural engineering
- Readers will learn how to apply ANNs for solutions to a broad range of engineering problems
- Includes AI-based software [ Matlab], which, will enable readers to verify the AI-based examples
1. Autonomous Design of Reinforced Concrete Beams and Columns Based on Artificial Neural Networks
2. Understanding artificial neural networks (ANNs): analogy to the biological neuron model
3. Factors influencing network trainings
4. Forward and backpropagation for artificial neural networks
5. Training methods: designs based on training entire data (TED), parallel training method (PTM), chained training scheme (CTS), and chained training scheme with revised sequence (CRS)
6. Singly reinforced concrete beams based on artificial neural networks
7. Design of Doubly Reinforced Concrete Beams based on Artificial Neural Network
8. Design of reinforced columns based on artificial neural networks
APPENDIX A. Manual to use MATLAB for training artificial neural networks (ANNs) B. MATLAB code for Revise Scenario 4 of Table 8.3
Dr. WonKee 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 hybrid composite structures and is the author of more than 100 technical papers and over 100 patents in both Korea and USA. In 2019, Dr. Hong also published Hybrid Composite Precast Systems: Numerical Investigation to Construction”, in the Woodhead Publishing Series in Civil and Structural Engineering, which has had an enthusiastic reception from readers. In this book, the author presented the basic concepts of artificial neural networks (ANNs) in Chapter 10 which contains an introduction to the implementation of AI-based neural networks applied to the design of reinforced concrete beams. More than 15 papers of Dr. Hong on the area of AI-based structural designs have been published or accepted in Journals published by Elsevier, Taylors and Francis. He also runs a YouTube channel (Deep learning for beginners; Won-Kee Hong, Kyung Hee University) on AI-based structural designs. Dr. Hong is a registered professional and structural engineer both in Korea and the United States.
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