Artificial intelligence-based design of reinforced concrete structures : artificial neural networks for engineering applications
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...
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| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Cambridge, MA :
Woodhead Publishing, an imprint of Elsevier,
[2023]
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| Series: | Woodhead Publishing series in civil and structural engineering.
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| Subjects: | |
| ISBN: | 9780443152535 0443152535 9780443152528 0443152527 |
| Physical Description: | 1 online resource. |
Cover
Table of contents
| LEADER | 08476cam a2200445Mi 4500 | ||
|---|---|---|---|
| 001 | kn-on1398519592 | ||
| 003 | OCoLC | ||
| 005 | 20240717213016.0 | ||
| 006 | m o d | ||
| 007 | cr cn||||||||| | ||
| 008 | 230504s2023 mau o 000 0 eng d | ||
| 040 | |a YDX |b eng |e rda |c YDX |d OCLCF |d DXU | ||
| 020 | |a 9780443152535 |q electronic book | ||
| 020 | |a 0443152535 |q electronic book | ||
| 020 | |z 9780443152528 |q paperback | ||
| 020 | |z 0443152527 |q paperback | ||
| 035 | |a (OCoLC)1398519592 | ||
| 100 | 1 | |a Hong, Won-Kee |c (Professor of architectural engineering), |e author. | |
| 245 | 1 | 0 | |a Artificial intelligence-based design of reinforced concrete structures : |b artificial neural networks for engineering applications / |c Won-Kee Hong. |
| 264 | 1 | |a Cambridge, MA : |b Woodhead Publishing, an imprint of Elsevier, |c [2023] | |
| 300 | |a 1 online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 490 | 1 | |a Woodhead Publishing series in civil and structural engineering | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a 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. | ||
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Reinforced concrete construction |x Data processing. | |
| 650 | 0 | |a Artificial intelligence |x Engineering applications. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 655 | 7 | |a elektronické knihy |7 fd186907 |2 czenas | |
| 655 | 9 | |a electronic books |2 eczenas | |
| 776 | 0 | 8 | |i ebook version : |z 9780443152535 |
| 776 | 0 | 8 | |c Original |z 0443152527 |z 9780443152528 |w (OCoLC)1347696117 |
| 830 | 0 | |a Woodhead Publishing series in civil and structural engineering. | |
| 856 | 4 | 0 | |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpAIBDRCS2/artificial-intelligence-based?kpromoter=marc |y Full text |
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