Artificial Intelligence-Based Design of Reinforced Concrete Structures : Artificial Neural Networks for Engineering Applications / Won-Kee Hong.

Format:

• Book

Author/Creator:

• Hong, Won-Kee (Professor of architectural engineering)

Series:

• Woodhead Publishing series in civil and structural engineering.
• Woodhead Publishing Series in Civil and Structural Engineering Series

Language:

English

Subjects (All):

• Artificial intelligence--Engineering applications.
• Neural networks (Computer science).
• Reinforced concrete construction--Data processing.

Physical Description:

1 online resource (510 pages)

Other Title:

Artificial Neural Networks for Engineering Applications

Place of Publication:

Cambridge, MA : Woodhead Publishing, Elsevier Ltd., [2023]

Summary:

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.

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
• 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
• 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.
• 4.1.4.6 Step 6: repeating steps 4 and 5 until a gradient descent shown in Eq. (4.1.4.3) becomes zero
• 4.1.5 Verification of predictions
• 4.2 A simple artificial neural network with forward propagation algorithm for a reinforced concrete beam
• 4.2.1 Forward propagation
• 4.2.2 Artificial neural networks based on backpropagation
• 4.2.2.1 Rate of changes in errors with respect to weight and bias
• 4.2.2.2 Calculation of mean squared error function
• 4.2.2.3 Updating weights connecting an output and neurons of the hidden layer
• 4.2.2.3.1 For weight ω21
• 4.2.2.3.2 For weight ω22
• 4.2.2.3.3 For weight ω23
• 4.2.2.3.4 For weight ω24
• 4.2.2.4 Updating weights connecting neurons of the hidden layer and inputs
• 4.2.2.4.1 For weight ω1
• 4.2.2.4.2 Summary of weight update
• ω1 to ω5
• 4.2.2.4.3 Summary of weight update
• ω6 to ω10
• 4.2.2.4.4 Summary of weight update
• ω11 to ω15
• 4.2.2.4.5 Summary of weight update
• ω16 to ω20
• 4.2.3 Designing RC beams based on reverse artificial neural networks
• 4.3 Conclusions
• 5. Training methods: designs based on training entire data, parallel training method, chained training scheme, and chained tra ...
• 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
• 5.6.1 Training entire data
• 5.6.2 Parallel training method
• 5.6.3 Chained training scheme
• 5.6.3.1 Why chained training scheme?
• 5.6.3.2 Selection of feature indexes based on the feature selection scores
• 5.6.3.3 Steps for chained training scheme
• 5.6.3.3.1 Step 1: Training an ANN on CBM
• 5.6.3.3.2 Step 2: Training an ANN on b.
• 5.6.3.3.3 Step 3: Training an ANN on ρt
• 5.6.3.3.4 Step 4: Train an ANN on ρc
• 5.7 Chained training scheme with revised sequence
• 5.7.1 Why chained training scheme with revised sequence?
• 5.7.2 CRS with revised training sequence to enhance training accuracies of PTM and CTS
• 5.7.3 Steps for CRS
• 5.7.3.1 Step 1: Training an ANN on CBM
• 5.7.3.2 Step 2: Training an ANN on ρt
• 5.7.3.3 Step 3: Training an ANN on b
• 5.7.3.4 Step 4: Training an ANN on ρc
• 5.7.4 CRS-trained accuracies based on features recommended by NCA only and extra features
• 5.7.5 Design verifications based on training methods
• 5.7.6 Verification charts
• 5.8 Conclusions
• Acknowledgment
• 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
• 6.2.1 Program to design a singly reinforced concrete section
• 6.2.2 Data generation code for a singly reinforced concrete section
• 6.3 Beam design by an ANN based on TED (training on entire inputs and outputs simultaneously)
• one forward problem and four rev ...
• 6.3.1 Design scenarios
• 6.3.2 Forward design
• 6.3.2.1 Forward training and design based on TED
• 6.3.2.2 Forward training and design based on TED and PTM
• 6.3.3 Reverse design
• 6.3.3.1 Design accuracies of reverse design based on TED
• 6.3.3.2 Reverse training and design based on TED, PTM, and CRS
• 6.3.4 Design accuracies with data qualities
• 6.4 Singly reinforced concrete beams based on shallow neural network
• 6.4.1 Reverse design scenarios (one forward problem and five reverse problems)
• 6.4.2 Formulation of training network for reverse designs
• 6.4.2.1 Direct method
• 6.4.2.2 Back-substitution method
• 6.4.2.2.1 Reverse Design 1 of Table 6.4.1a
• 6.4.2.2.2 Reverse Design 2 of Table 6.4.1b.
• 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
• 6.5.3 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.
• 7.5.2.3.1 Application to a reverse design with nine inputs (φMn, μφ, Mu, MD, ML, L, b, fy, and f'c) and nine outputs (h, d, ρrt, ρrc,.

Notes:

• Includes bibliographical references and index.
• Description based on print version record.

Other Format:

Print version: Hong, Won-Kee Artificial Intelligence-Based Design of Reinforced Concrete Structures

ISBN:

9780443152535

OCLC:

1378392346