Automation and Computational Intelligence for Road Maintenance and Management

Name: Automation and Computational Intelligence for Road Maintenance and Management | Advances and Applications
Brand: Wiley
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Advances and Applications

Hamzeh Zakeri Fereidoon Moghadas Nejad Amir H. Gandomi(Author)

Wiley (Publisher)

1st Edition

Published on 17. June 2022

544 pages

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978-1-119-80065-1 (ISBN)

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Automation and Computational Intelligence for Road Maintenance and Management

A comprehensive computational intelligence toolbox for solving problems in infrastructure management

In Automation and Computational Intelligence for Road Maintenance and Management, a team of accomplished researchers delivers an incisive reference that covers the latest developments in computer technology infrastructure management. The book contains an overview of foundational and emerging technologies and methods in both automation and computational intelligence, as well as detailed presentations of specific methodologies.

The distinguished authors emphasize the most recent advances in the maintenance and management of infrastructure robotics, automated inspection, remote sensing, and the applications of new and emerging computing technologies, including artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, knowledge discovery and engineering, and more.

Automation and Computational Intelligence for Road Maintenance and Management explores a universal synthesis of the cutting edge in parameters and indices to evaluate models. It also includes:

* Thorough introductions to management science and the latest methods of automation and the structure and framework of automation and computing intelligence

* Comprehensive explorations of advanced image processing techniques, recent advances in fuzzy, and diagnosis automation

* Practical discussions of segmentation and fragmentation and different types of features and feature extraction methods

* In-depth examinations of methods of classification along with various developed methodologies and models of quantification, evaluation, and indexing in automation

Perfect for postgraduate students in road and transportation engineering, evaluation, and assessment, Automation and Computational Intelligence for Road Maintenance and Management will also earn a place in the libraries of researchers interested in or working with the evaluation and assessment of infrastructure.

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Content

Cover
Title Page
Copyright Page
Contents
Dedication
Preface
Author Biography
Chapter 1 Concepts and Foundations Automation and Emerging Technologies
1.1 Introduction
1.2 Structure and Framework of Automation and Key Performance Indexes (KPIs)
1.3 Advanced Image Processing Techniques
1.4 Fuzzy and Its Recent Advances
1.5 Automatic Detection and Its Applications in Infrastructure
1.6 Feature Extraction and Fragmentation Methods
1.7 Feature Prioritization and Selection Methods
1.8 Classification Methods and Its Applications in Infrastructure Management
1.9 Models of Performance Measures and Quantification in Automation
1.10 Nature-Inspired Optimization Algorithms (NIOAS)
1.11 Summary and Conclusion
1.12 Questions and Exercise
Chapter 2 The Structure and Framework of Automation and Key Performance Indices (KPIs)
2.1 Introduction
2.2 Macro Plan and Architecture of Automation
2.2.1 Infrastructure Automation
2.2.2 Importance of Infrastructure Automation Evaluation
2.3 A General Framework and Design of Automation
2.4 Infrastructure Condition Index and Its Relationship with Cracking
2.4.1 Road Condition Index
2.4.2 Bridge Condition Index
2.4.3 Tunnel Condition Index
2.5 Automation, Emerging Technologies, and Futures Studies
2.6 Summary and Conclusion
2.7 Questions
Further Reading
Chapter 3 Advanced Images Processing Techniques
Introduction
3.1 Preprocessing (PPS)
3.1.1 Edge Preservation Index (EPI)
3.1.2 Edge-Strength Similarity-Based Image Quality Metric (ESSIM)
3.1.3 QILV Index
3.1.4 Structural Content Index (SCI)
3.1.5 Signal-To-Noise Ratio Index (PSNR)
3.1.6 Computational time index (CTI)
3.2 Preprocessing Using Single-Level Methods
3.2.1 Single-Level Methods
3.2.2 Linear Location Filter (LLF)
3.2.3 Median Filter
3.2.4 Wiener Filter
3.3 Preprocessing Using Multilevel (Multiresolution) Methods
3.3.1 Wavelet Method
3.3.2 Ridgelet Transform
3.3.3 Curvelet Transform
3.3.4 Decompaction and Reconstruction Images Using Shearlet Transform (SHT)
3.3.5 Discrete Shearlet Transform (DST)
3.3.6 Shearlet Decompaction and Reconstruction
3.3.7 Shearlet and Wavelet Comparison
3.3.8 Complex Shearlet Transform
3.3.9 Complex Shearlet Transform for Image Enhancement
3.3.10 Low and High frequencies of Complex Shearlet Transform for Image Denoising
3.4 General Comparison of Single/Multilevel Methods and Selection of Methods for Noise Removal and Image Enhancement
3.5 Application of Preprocessing
3.5.1 Pavement Surface Drainage Condition Assessment
3.6 Summary and Conclusion
3.7 Questions and Exercises
Chapter 4 Fuzzy and Its Recent Advances
4.1 Introduction
4.1.1 Type-1 Fuzzy Set Theory
4.1.2 Type-2 Fuzzy Set Theory
4.1.3 a-Plane Representation of General Type-2 Fuzzy Sets
4.1.4 Type-Reduction
4.1.5 Defuzzification
4.1.6 Type-3 Fuzzy Logic Sets
4.2 Ambiguity Modeling in the Fuzzy Methods
4.2.1 Background of General Type-2 Fuzzy Sets
4.3 Theory of Automatic Methods for MF Generation
4.3.1 Automatic Procedure to Generate a 3D Membership Function
4.4 Steps and Components of General 3D Type-2 Fuzzy Logic Systems (G3DT2 FL)
4.4.1 General 3D Type-2 Fuzzy Logic Systems (G3DT2 FL)
4.5 General 3D Type-2 Polar Fuzzy Method
4.5.1 Automatic MF Generator
4.5.2 A Measure of Ultrafuzziness
4.5.3 Theoretic Operations of 3D Type-2 Fuzzy Sets in the Polar Frame
4.5.4 Representation of Fuzzy 3D Polar Rules
4.5.5 ?-Slice and a - Planes
4.6 Computational Performance (CP)
4.7 Application of G3DT2FLS in Pattern Recognition
4.7.1 Examples of the Application of Fuzzy Methods in Infrastructure Management
4.8 Summary and Conclusion
4.9 Questions and Exercises
Further Reading
Chapter 5 Automatic Detection and Its Applications in Infrastructure
5.1 Introduction
5.1.1 Photometric Hypotheses (PH)
5.1.2 Geometric and Photometric Hypotheses (GPH)
5.1.3 Geometric Hypotheses (GH)
5.1.4 Transform Hypotheses (TH)
5.2 The Framework for Automatic Detection of Abnormalities in Infrastructure Images
5.2.1 Wavelet Method
5.2.2 High Amplitude Wavelet Coefficient Percentage (HAWCP)
5.2.3 High-Frequency Wavelet Energy Percentage (HFWEP)
5.2.4 Wavelet Standard Deviation (WSTD)
5.2.5 Moments of Wavelet
5.2.6 High Amplitude Shearlet Coefficient Percentage (HASHCP)
5.2.7 High-Frequency Shearlet Energy Percentage (HFSHEP)
5.2.8 Fractal Index
5.2.9 Moments of Complex Shearlet
5.2.10 Central Moments q
5.2.11 Hu Moments
5.2.12 Bamieh Moments
5.2.13 Zernike Moments
5.2.14 Statistic of Complex Shearlet
5.2.15 Contrast of Complex Shearlet
5.2.16 Correlation of Complex Shearlet
5.2.17 Uniformity of Complex Shearlet
5.2.18 Homogeneity of Complex Shearlet
5.2.19 Entropy of Complex Shearlet
5.2.20 Local Standard Deviation of Complex Shearlet Index (F_Local_STD)
5.3 Summary and Conclusion
5.4 Questions and Exercises
Further Reading
Chapter 6 Feature Extraction and Fragmentation Methods
6.1 Introduction
6.2 Low-Level Feature Extraction Methods
6.3 Shape-Based Feature (SBF)
6.3.1 Center of Gravity (COG) or Center of Area (COA)
6.3.2 Axis of Least Inertia (ALI)
6.3.3 Average Bending Energy
6.3.4 Eccentricity Index (ECI)
6.3.5 Circularity Ratio (CIR)
6.3.6 Ellipse Variance Feature (EVF)
6.3.7 Rectangularity Feature (REF)
6.3.8 Convexity Feature (COF)
6.3.9 Euler Number Feature (ENF)
6.3.10 Profiles Feature (PRF)
6.4 1D Function-Based Features for Shape Representation
6.4.1 Complex Coordinates Feature (CCF)
6.4.2 Extracting Edge Characteristics Using Complex Coordinates
6.4.3 Edge Detection Using Even and Odd Shearlet Symmetric Generators
6.4.4 Object Detection and Isolation Using the Shearlet Coefficient Feature (SCF)
6.5 Polygonal-Based Features (PBF)
6.6 Spatial Interrelation Feature (SIF)
6.7 Moments Features (MFE)
6.8 Scale Space Approaches for Feature Extraction (SSA)
6.9 Shape Transform Features (STF)
6.9.1 Radon Transform Features (RTF)
6.9.2 Linear Radon Transform
6.9.3 Translation of RT
6.9.4 Scaling of RT
6.9.5 Point and Line Transform Using RT
6.9.6 RT in Sparse Objects
6.9.7 Point and Line in RT
6.10 Various Case-Based Examples in Infrastructures Management
6.10.1 Case 1: Feature Extraction from Polypropylene Modified Bitumen Optical Microscopy Images
6.10.2 Ratio of Number of Black Pixels to the Number of Total Pixels (RBT)
6.10.3 Ratio of Number of Black Pixels to the Number of Total Pixels in Watershed Segmentation (RWS)
6.10.4 Number and Average Area of the White Circular Objects in the Binary Image (The number of circular objects [NCO] & ACO)
6.10.5 Entropy of the Image
6.10.6 Radon Transform Maximum Value (RTMV)
6.10.7 Entropy of Radon Transform (ERT)
6.10.8 High Amplitude Radon Percentage (HARP)
6.10.9 High-Energy Radon Percentage (HERP)
6.10.10 Standard Deviation of Radon Transform (STDR)
6.10.11 Qth-Moment of Radon Transform (QMRT)
6.10.12 Case 2: Image-Based Feature Extraction for Pavement Skid Evaluation
6.10.13 Case 3: Image-Based Feature Extraction for Pavement Texture Drainage Capability Evaluation
6.10.14 Case 4: Image-Based Features Extraction in Pavement Cracking Evaluation
6.10.15 Automatic Extraction of Crack Features
6.10.16 Extraction of Crack Skeleton Using Shearlet Complex Method
6.10.17 Calculate Crack Width Feature Using External Multiplication Method
6.10.18 Detection of Crack Starting Feature (Crack Core) Using EPA Emperor Penguin Metaheuristic Algorithm
6.10.19 Selection of Crack Root Feature Based on Geodetic Distance
6.10.20 Determining Coordinates of the Crack Core as the Optimal Center at the Failure Level using EPA Method
6.10.21 Development of New Features for Crack Evaluation Based on Graph Energy
6.10.22 Crack Homogeneity Feature Based on Graph Energy Theory
6.10.23 Spall Type 1 Feature: Crack Based on Graph Energy Theory in Crack Width Mode
6.10.24 General Crack Index Based on Graph Energy Theory
6.11 Summary and Conclusion
6.12 Questions and Exercises
Further Reading
Chapter 7 Feature Prioritization and Selection Methods
7.1 Introduction
7.2 A Variety of Features Selection Methods
7.2.1 Filter Methods
7.2.2 Correlation Criteria
7.2.3 Mutual Information (MI)
7.2.4 Wrapper Methods
7.2.5 Sequential Feature Selection (SFS) Algorithm
7.2.6 Heuristic Search Algorithm (HAS)
7.2.7 Embedded Methods
7.2.8 Hybrid Methods
7.2.9 Feature Selection Using the Fuzzy Entropy Method
7.2.10 Hybrid-Based Feature Selection Using the Hierarchical Fuzzy Entropy Method
7.2.11 Step 1: Measure Similarity Index and Evaluate Features
7.2.12 Step 2: Final Feature Vector
7.3 Classification Algorithm Based on Modified Support Vectors for Feature Selection - CDFESVM
7.3.1 Methods for Determining the Fuzzy Membership Function in Feature Selection
7.4 Summary and Conclusion
7.5Questions and Exercises
Further Reading
Chapter 8 Classification Methods and Its Applications in Infrastructure Management
8.1 Introduction
8.2 Classification Methods
8.2.1 Naive Bayes Classification
8.2.2 Decision Trees
8.2.3 Logistic Regression
8.2.4 k-Nearest Neighbors (kNN)
8.2.5 Ensemble Techniques
8.2.6 Adaptive Boosting (AdaBoost)
8.2.7 Artificial Neural Network
8.2.8 Support Vector Machine
8.2.9 Fuzzy Support Vector Machine (FSVM)
8.2.10 Twin Support Vector Machine (TSVM)
8.2.11 Fuzzy Twin Support Vector Machine (FTSVM)
8.2.12 Entropy and Its Application FSVM
8.2.13 Development of Entropy Fuzzy Coordinate Descent Support Vector Machine (EFCDSVM)
8.2.14 Development of a New Support Vector Machine in Polar Frame (PSVM)
8.2.15 Case Study: Pavement Crack Classification Based on PSVM
8.3 Summary and Conclusion
8.4 Questions and Exercises
Further Reading
Chapter 9 Models of Performance Measures and Quantification in Automation
9.1 Introduction
9.2 Basic Definitions
9.2.1 Confusion Matrix
9.2.2 Main Metrics
9.2.3 Accuracy Indexes
9.2.4 Time (Speed)
9.3 Database Modeling and Model Selection
9.3.1 Different Parts of the Data
9.3.2 Cross Validation
9.3.3 Regularization Techniques and Overfitting
9.4 Performance Evaluations and Main Metrics
9.4.1 General Statistics
9.4.2 Basic Rations
9.4.3 Rations of Ratios
9.4.4 Additional Statistics
9.4.5 Operating Characteristic
9.5 Case Studies
9.5.1 Case 1: The Confusion Matrix for Evaluating Drainage of Pavement Surface
9.5.2 Case 2: Metrics for Pavement Creak Detection Based on Deep Learning Using Transfer Learning
9.5.3 Case 3: The Confusion Matrix for Evaluating Pavement Crack Classification
9.5.4 Case 4: Quality Evaluation for Determining Bulk Density of Aggregates
9.6 Summary and Conclusion
9.7 Questions and Exercises
Further Reading
Chapter 10 Nature-Inspired Optimization Algorithms (NIOAs)
10.1 Introduction
10.2 General Framework and Levels of Designing Nature-Inspired Optimization Algorithms (NIOAs)
10.3 Basic Principles of Important Nature-Inspired Algorithms (NIOAs)
10.3.1 Genetic Algorithm (GA)
10.3.2 Particle Swarm Optimization (PSO) Algorithm
10.3.3 Artificial Bee Colony (ABC) Algorithm
10.3.4 Bat Algorithm (BA)
10.3.5 Immune Algorithm (IA)
10.3.6 Firefly Algorithm (FA)
10.3.7 Cuckoo Search (CS) Algorithm
10.3.8 Gray Wolf Optimizer (GWO)
10.3.9 Krill Herd Algorithm (KHA)
10.3.10 Emperor Penguin Algorithms (EPA)
10.3.11 Hybrid Optimization Methods
10.4 Summary and Conclusion
10.5 Questions and Exercises
Further Reading
Appendix A Data Sets and Codes
Appendix B The Glossary of Nature-Inspired Optimization Algorithms (NIOAS)
B.1 Introduction
Appendix C Sample Code for Feature Selection
Index
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Automation and Computational Intelligence for Road Maintenance and Management

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