Artificial intelligence-based design of reinforced concrete structures : artificial neural networks for engineering applications

• Main Author: Hong, Won-Kee (Professor of architectural engineering) (Author)
• Format: Electronic eBook
• Language: English
• Published: Cambridge, MA : Woodhead Publishing, an imprint of Elsevier, [2023]
• Series: Woodhead Publishing series in civil and structural engineering.
• Subjects: Reinforced concrete construction > Data processing.; Artificial intelligence > Engineering applications.; Neural networks (Computer science); elektronické knihy; electronic books
• ISBN: 9780443152535; 0443152535; 9780443152528; 0443152527
• Physical Description: 1 online resource.

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 con...

Table of Contents:

• Front Cover
• Artificial Intelligence-Based Design of Reinforced Concrete Structures
• Artificial Intelligence-Based Design of Reinforced Concrete Structures: Artificial Neural Networks for Engineering Applicatio ...
• Copyright
• Contents
• Preface
• Acknowledgments
• 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
• References
• 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
• 2.2.1 Why activation functions?
• 2.2.2 Activation functions for squashing the linear part of neurons
• 2.2.3 Types of activation functions
• 2.2.3.1 tanh(x)
• 2.2.3.2 Sigmoid
• 2.2.3.3 Rectified linear unit function
• References
• 3
• Factors influencing network trainings
• 3.1 Requirement for good training accuracies
• 3.1.1 Training with extrapolated datasets
• 3.1.2 A lack of feature indexes
• 3.1.3 Discontinuous output parameters
• 3.1.4 The following steps can also be taken to efficiently to avoid overfitting
• 3.1.5 Input conflict for reverse designs
• 3.2 Data initialization
• 3.2.1 Why initialization?
• 3.2.1.1 Vanishing and exploding gradient issues due to wide distribution of neural outputs
• 3.2.1.2 Weights narrowly distributed to prevent vanishing and exploding gradient issues
• 3.2.2 How to initialize neural network parameters effectively: avoiding vanishing gradients due to large standard deviations
• 3.2.3 Types of initializations.
• 3.2.3.1 How initializations are performed
• 3.2.3.2 Initializations of Xavier (or Glorot) and He et al
• 3.3 Data normalization
• 3.3.1 Why normalization for network training?
• 3.3.2 How to normalize neural network parameters effectively
• 3.3.3 Verification of training
• 3.3.4 Recovery scale of original dataset
• 3.4 Multilayer perception
• 3.4.1 Understanding artificial neural networks with multiple layers and neurons
• 3.4.2 What are the neurons, weights, bias, and activation functions used in artificial neural networks for structural applications?
• 3.4.3 Feedforward networks connected by weights and biases
• 3.5 Training, validation, testing, and design
• 3.5.1 Conditions for good artificial neural networks
• 3.5.2 Validation of artificial neural network
• 3.6 Backpropagation for adjusting weights and bias
• 3.6.1 Why backpropagations?
• 3.6.2 Backpropagation minimizing cost functions
• 3.6.3 Chain rule for backpropagation
• 3.7 Conclusions
• References
• 4
• Forward and backpropagation for artificial neural networks
• 4.1 Gradient descent algorithm
• 4.1.1 Introduction
• 4.1.2 Problem example
• 4.1.3 Gradient descent for calculating a single fitting variable
• 4.1.3.1 Weight and bias
• 4.1.3.1.1 Step 1: Initialization
• 4.1.3.2 Loss function
• 4.1.3.2.1 Step 2: calculating MSE
• 4.1.3.3 Learning rate
• 4.1.4 Gradient descent to determine multiple fitting variables
• 4.1.4.1 Step 1: establishing an initial fitting line
• 4.1.4.2 Step 2: calculating a loss function as a function of two fitting variables
• 4.1.4.3 Step 3: calculating a gradient of a loss function with respect to weight and bias
• 4.1.4.4 Step 4: minimizing a loss function by converging gradient (a slope) descents to zero
• 4.1.4.5 Step 5: selecting a learning rate to calculate step size.
• 6.4.3 Design verification
• 6.4.3.1 Forward designs
• 6.4.3.1.1 Training verifications
• 6.4.3.1.2 Design verifications
• 6.4.3.2 Reverse designs
• 6.4.3.2.1 Reverse Design Scenario 1
• 6.4.3.2.2 Reverse Design Scenario 2
• 6.4.3.2.3 Reverse Design Scenario 3
• 6.4.3.2.4 Reverse Design Scenario 4
• 6.4.3.2.5 Reverse Design Scenario 5
• 6.5 Design of singly reinforced concrete beams (machine learning)
• 6.5.1 Feature selection-based machine learning for design of singly reinforced concrete beams
• 6.5.2 Interpretation of feature selections (for training structural data)
• 6.5.2.1 Overview of feature selection
• 6.5.2.2 Training results of machine learning based on forward design
• 6.5.2.2.1 Data for training
• 6.5.2.2.2 Training with nonchained method based on feature scores
• 6.5.2.2.3 Training with chained method based on feature scores
• 6.5.2.3 Design accuracies based on forward design
• 6.5.2.4 Reverse design implanting artificial neural genes on an input-side
• 6.5.2.4 Reverse design implanting artificial neural genes on an input-side
• 6.5.3 Rationale for feature selection
• 6.6 Recommendations and conclusions
• Reference
• 7
• Design of doubly reinforced concrete beams based on artificial neural network (deep learning) and regression models (ma ...
• 7.1 Introduction
• 7.2 Motivation of the artificial neural network-based design
• 7.2.1 Previous researches
• 7.2.2 Importance of the chapter
• 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
• 7.5.1 Design scenarios
• 7.5.2 Design of doubly reinforced ductile concrete beam
• 7.5.2.1 Forward design
• 7.5.2.2 Reverse design
• 7.5.2.3 Formulation of back-substitution method.