Automation and Computational Intelligence for Road Maintenance and Management : Advances and Applications

• Autore: Zakeri Hamzeh
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2022; ©2022
• Descrizione fisica: 1 online resource (547 pages)
• Altri autori: NejadFereidoon Moghadas GandomiAmir H
• Note generali: Description based upon print version of record.
• Nota di contenuto: 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 ϑ-Slice and α − 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.
• Titolo autorizzato: Automation and Computational Intelligence for Road Maintenance and Management
• ISBN: 1-119-80067-6; 1-119-80065-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910580257303321
• Lo trovi qui: Univ. Federico II