Automation and computational intelligence for road maintenance and management : advances and applications /

"This book is a reference that makes a connection between developments in computer technology infrastructure management. It provides a unique form for computational techniques and state-of-the-art automation applications. It contains the fundamental emerging technologies and methods in both aut...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Zakeri, Hamzeh (Autor), Nejad, Fereidoon Moghadas (Autor), Gandomi, Amir Hossein (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc., 2022.
Temas:
Road construction industry > Automation.
Computational intelligence.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.
  • 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] &amp
  • 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.

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