Artificial 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.
1. 8.1: Introduction - p. 329 2. 8.2: Design of concrete columns - p. 330 3. 8.3: Generation of big data - p. 331 4. 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 - p. 331 5. 8.5: Verification of an ANN-based optimization by ANN-based Lagrange and large datasets - p. 387 6. 8.6: Recommendations and conclusions - p. 390 7. Appendix A: Manual to use MATLAB for training artificial neural networks - p. 395 8. Appendix B: MATLAB code for Revise Scenario 4 of Table 8.4.1 - p. 415 9. Index - p. 489
Artificial 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.
1. 8.1: Introduction - p. 329 2. 8.2: Design of concrete columns - p. 330 3. 8.3: Generation of big data - p. 331 4. 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 - p. 331 5. 8.5: Verification of an ANN-based optimization by ANN-based Lagrange and large datasets - p. 387 6. 8.6: Recommendations and conclusions - p. 390 7. Appendix A: Manual to use MATLAB for training artificial neural networks - p. 395 8. Appendix B: MATLAB code for Revise Scenario 4 of Table 8.4.1 - p. 415 9. Index - p. 489
