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Artificial Intelligence-Based Design of Reinforced Concrete Structures
Artificial Neural Networks for Engineering Applications
- 1st Edition - April 29, 2023
- Author: Won-Kee Hong
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 2 5 2 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 2 5 3 - 5
Artificial Intelligence-Based Design of Reinforced Concrete Structures: Artificial Neural Networks for Engineering Applications is an essential reference resource for readers w… Read more
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Request a sales quoteArtificial Intelligence-Based Design of Reinforced Concrete Structures: Artificial Neural Networks for Engineering Applications is an essential reference resource for 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. Plenty of examples based on real engineering problems and solutions are included to help readers better understand important concepts.
- Helps civil engineers understand 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 engineer
- Teaches readers how to apply ANNs as solutions for a broad range of engineering problems
- Includes AI-based software [MATLAB], which will enable readers to verify AI-based examples
Academic researchers, designers and practicing engineers in civil engineering especially those interested in using Artificial Neural Networks (ANNs) in the design, management, and maintenance of civil structures, Students in civil engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- 1. Design of reinforced concrete beams and columns based on artificial neural networks
- 1.1. What can be learned from this book?
- 1.2. An evolution of artificial neural networks in civil engineering
- 1.3. Common machine learning versus artificial neural networks with deep learning using deep layers
- 1.4. Accuracy and interpretability of common artificial intelligence models
- 2. Understanding artificial neural networks: analogy to the biological neuron model
- 2.1. A learning and memory capability similar to that of the human brain
- 2.2. Activation functions
- 3. Factors influencing network trainings
- 3.1. Requirement for good training accuracies
- 3.2. Data initialization
- 3.3. Data normalization
- 3.4. Multilayer perception
- 3.5. Training, validation, testing, and design
- 3.6. Backpropagation for adjusting weights and bias
- 3.7. Conclusions
- 4. Forward and backpropagation for artificial neural networks
- 4.1. Gradient descent algorithm
- 4.2. A simple artificial neural network with forward propagation algorithm for a reinforced concrete beam
- 4.3. Conclusions
- 5. Training methods: designs based on training entire data, parallel training method, chained training scheme, and chained training scheme with revised sequence
- 5.1. Past studies
- 5.2. Significance of the chapter
- 5.3. Machine learning models versus deep layers based on artificial neural networks for structural engineering applications
- 5.4. Artificial neural networks and big data generation
- 5.5. Feature selection scores
- 5.6. Training methods, TED, PTM, CTS, and CRS
- 5.7. Chained training scheme with revised sequence
- 5.8. Conclusions
- 6. Singly reinforced concrete beams based on regression models and artificial neural networks
- 6.1. Significance of the chapter
- 6.2. Generation of big data
- 6.3. Beam design by an ANN based on TED (training on entire inputs and outputs simultaneously); one forward problem and four reverse problems
- 6.4. Singly reinforced concrete beams based on shallow neural network
- 6.5. Design of singly reinforced concrete beams (machine learning)
- 6.6. Recommendations and conclusions
- 7. Design of doubly reinforced concrete beams based on artificial neural network (deep learning) and regression models (machine learning)
- 7.1. Introduction
- 7.2. Motivation of the artificial neural network–based design
- 7.3. Deep neural networks for structural engineering
- 7.4. Generation of large structural datasets and network training
- 7.5. Design of doubly reinforced concrete beams based on artificial neural network
- 7.6. ANN-based reverse design charts based on ACI 318-19
- 7.7. Reverse designs using Gaussian process regression models enhanced by sequence training/designing technique based on feature selection algorithms
- 7.8. Feature selections for training structural datasets
- 7.9. Chained training scheme with Revised Sequence
- 7.10. Conclusion
- 8. Design of reinforced columns based on artificial neural networks
- 8.1. Introduction
- 8.2. Design of concrete columns
- 8.3. Generation of big data
- 8.4. Column design by artificial neural networks based on training networks on entire data, parallel training method, and chained revised sequence: one forward design and five reverse designs
- 8.5. Verification of an ANN-based optimization by ANN-based Lagrange and large datasets
- 8.6. Recommendations and conclusions
- Appendix A: Manual to use MATLAB for training artificial neural networks
- Appendix B: MATLAB code for Revise Scenario 4 of Table 8.4.1
- Index
- No. of pages: 508
- Language: English
- Edition: 1
- Published: April 29, 2023
- Imprint: Woodhead Publishing
- Paperback ISBN: 9780443152528
- eBook ISBN: 9780443152535
WH
Won-Kee Hong
Dr. Won‐Kee 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.
Affiliations and expertise
Dr. Won‐Kee Hong is a professor of Architectural Engineering at Kyung Hee University, South Korea.Read Artificial Intelligence-Based Design of Reinforced Concrete Structures on ScienceDirect