Recent Advances in Structural Health Monitoring Research in Australia
Published on 31. May 2022
412 pages
978-1-68507-609-2 (ISBN)
Description
Structural Health Monitoring (SHM), a process of utilising on-structure or remote sensing systems and/or imaging techniques to monitor the performance of structures and evaluate their health conditions, has rapidly grown worldwide on its research and industrial practices in recent years. Recent disastrous bridge failures, such as the collapses of the Nanfang''ao tied-arch bridge in Taiwan, the Wuxi National Highway 312 overpass in China, and the Pont de Mirepoix suspension bridge in France, all in the year 2019, and the Mexico City Metro overpass collapse in 2021, have further reminded us of the importance of structural health monitoring for civil infrastructures. During the last three decades, SHM has attracted enormous research efforts around the world because it targets on monitoring structural conditions to prevent catastrophic failure and to provide quantitative data for engineers and infrastructure owners to design reliable and economical asset management plans. This book showcases the recent advancement in SHM research, especially for civil engineering applications in Australia, covering the state-of-the-art SHM technologies together with its latest developments and successful applications. The book provides a glance on the research outcomes in SHM-related areas delivered by some of the experts in Australian universities. This book is launched to mark the significant milestone of the 10th Anniversary of Australian Network of Structural Health Monitoring (ANSHM) and its contribution to the SHM research and practice over the past 12 years since its inception in 2009. The preparation of this book for an intended completion in 2020 was significantly delayed due to the impact of the COVID-19 pandemic. The Network, comprising leading Australian SHM experts, aims to promote and advance SHM application, education, research and development in Australia. Although the title of the book highlights recent SHM advances in Australia, the technologies and approaches described can be applied around the world.
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06/2022
Nova Science Publishers Inc
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Content
- Intro
- Contents
- Foreword
- Preface
- Chapter 1
- Damage Detection and Model Updating of Civil Engineering Structures
- Abstract
- Introduction
- Damage Detection for Civil Structures
- Modal-Based Damage Detection Using Direct Approaches
- Overview of Direct Approaches
- Recent Developments in Direct Approaches
- Damage Detection in Suspension Bridges
- Damage Detection in Hyperbolic Cooling Towers
- Selected Study: Damage Detection in Asymmetric Buildings Using Improved Multi-MSE and -MF Criteria
- Methodology
- Modified MSE Index
- Multi-Criteria Index
- Verification
- Modal Parameter-Based Damage Detection Using Correlation Approaches
- Overview of Correlation Approaches
- Recent Developments in Correlation-Based Approaches
- Damage Detection Using Direct MSE Change Correlation and Multi-Layer Genetic Algorithm
- Damage Detection for Mass-Varied Structures Using MKE Correlation
- Selected Study: Correlation-Based MSEE Approach for Complex Truss Structures
- Methodology
- Change in Elemental MSEE
- Change in Total MSEE
- Damage Identification Using MSEE Change
- Sensitivity-Weighted Search Space (SWSS) Technique
- Verification
- Deflection-Based Damage Detection
- Overview of Deflection-Based Damage Detection
- Damage Detection for Simply Supported Beam Using Direct Static Deflection Changes
- Damage Detection in Simply Supported (SS) Beam with Single Damage
- Locating Damage
- Quantifying Damage
- Damage Detection in Simply Supported Beam with Double Damage
- Locating Damage
- Quantifying Damage
- Verification
- Damage Detection Using Artificial Neural Networks (ANNs)
- Overview of ANN-Based Damage Detection
- Recent Developments in ANN-Based Damage Detection
- Damage Detection in Slab-on Girder Bridges Using MSE-Damage Index and ANNs
- Damage Detection in Hyperbolic Cooling Towers Using Mode Shape Curvature and ANNs
- Selected Study: Damage Detection in Deck Type Arch Bridges Using Combined MSE- and MF-Based ANN
- Description of Examined Structure
- Methodology
- Input Parameters
- ANN Architecture
- Results and Discussions
- Damage Detection Based on Time-Series Analysis
- Overview of Time-Series Analysis-Based Damage Detection
- Structural Deterioration Identification Using Enhanced Autoregressive Time-Series Analysis
- Methodology
- AR Model Enhanced with Advanced Data Normalization
- Best-Fit Model Order (BMO) Estimation
- Deterioration Indicator
- Verification
- Model Updating for Civil Structures
- Overview of Model Updating
- Deterministic Sensitivity-based Model Updating Incorporating Uncertainties
- Description of Examined Structure and Initial FE Model
- Methodology
- Sensitivity Analysis
- Model Updating Procedure
- Objective Function
- Results and Discussions
- Probabilistic Model Updating Using Gaussian Process
- Methodology
- General Formulation of Model Updating
- Gaussian Process
- Modular Bayesian Approach
- Module 1: Gaussian Process for Numerical Model
- Module 2: Gaussian Process for Discrepancy Function
- Module 3: Posterior of Calibration Parameters
- Module 4: Prediction of Experimental Response and Discrepancy Function
- Model Updating for a Large-Scale Box Girder Bridge
- Conclusion
- References
- Chapter 2
- Effective Sensitivity-Based Model Updating of Cable-Stayed Bridges Considering Monitoring Data Variability
- Abstract
- Introduction
- Structural Health Monitoring of Bridges
- SHM Applications for Bridges
- Current Practices of SHM for Bridges
- Data-Based Approaches
- Model-Based Approaches
- Finite Element Model Updating
- Direct Methods
- Iterative Methods
- Stochastic Methods
- Sensitivity-Based Model Updating Method
- Phu My Cable-Stayed Bridge - A Case Study
- Descriptions of the Bridge and SHM System
- Structural Health Monitoring Data
- Initial Finite Element Model
- Model Updating Outcomes
- Selection of Matching Modes
- Selection of Updating Parameters
- Model Updating Results
- Conclusion
- References
- Chapter 3
- Drive-by Bridge Structural Health Monitoring: A Case Study on Modal Identification Using a Mobile Sensory System
- Abstract
- Introduction
- Conceptual Framework
- Mobile Sensor Networks for Drive-By Bridge SHM
- Theoretical Background and Approaches
- Vehicle-Bridge Interaction System
- The SSA-BSS
- Singular Spectrum Analysis
- Embedding
- Singular Value Decomposition
- Grouping
- Skew Diagonal Averaging
- Blind Modal Identification with SSA
- Application to a Cable-Stayed Bridge
- Description of the Field Bridge
- Bridge Modal Identification Using the Bridge Long-term Monitoring System
- Drive-By Modal Identification Using an Instrumented Vehicle
- Conclusion
- References
- Chapter 4
- Damage Detection of Partially Immersed Plates Using Guided Waves
- Abstract
- Introduction
- Guided Waves
- The Influence of Water on the Guided Wave Propagation
- Experimental Setup
- Experimental Results
- Wave Mode Identification
- Scattering Characteristics of the Quasi-Scholte Waves at Blind Holes
- 3D FE Simulations
- Experiment Verification
- Intact Plate with One Side Loaded with Water
- Damaged Plate with a Blind Hole at the Plate-water Interface
- Scattering Directivity Patterns (SDPs)
- Discussion of Characterizing Damage in Submerged Plates Using QS Wave
- Conclusion
- Acknowledgment
- References
- Chapter 5
- Classification for Images of Corroded Steel by Image Processing Technology
- Abstract
- Introduction
- Previous Study
- Advanced Technology for Bridge Inspection
- Image Processing for Bridges
- Method
- Corrosion Tests
- Classification through CNN Analysis
- Results and Discussion
- Training and Verification of the Classifier Model
- Comparison of the Each Environment Classifiers
- Conclusion
- References
- Chapter 6
- Data-Driven Structural Health Monitoring Based on Deep Learning Techniques
- Abstract
- Introduction
- Autoencoder Neural Networks Framework for Structural Health Monitoring
- Autoencoder
- Sparse Autoencoder
- Autoencoder Based Framework (AutoDNet)
- Dimensionality Reduction
- Relationship Learning
- Sparse Autoencoder Based Framework (SAF)
- Sparse Dimensionality Reduction
- Training and Fine-Tuning of the Full Network
- Deep Residual Network Framework for Structural Health Monitoring
- Residual Network
- Residual Learning
- The Proposed Deep ResNet Framework
- Architecture
- Numerical Studies
- Numerical Structure and Finite Element Model
- Data Generation
- Performance Evaluation
- Experimental Verifications
- Experimental Model and Initial Model Updating
- Data Generation
- The Network Structures
- Training Performance and Damage Identification Results
- Conclusion
- Acknowledgments
- References
- Chapter 7
- Robustness of Deep Transfer Learning-Based Crack Detection against Uncertainty in Hyperparameter Tuning and Input Data
- Abstract
- Introduction
- Background
- Creation of Image Datasets
- Methodology
- Results and Discussion
- Case Study 1 - Network Performance against Batch Size Variation
- Case Study 2 - Network Performance against Image Noise
- Conclusion
- References
- Chapter 8
- Smart Automated Fault Detection for Improved Road Maintenance Planning in Australia
- Abstract
- Introduction
- Background
- Pavement Cracking
- Using AI for Road Fault Detection
- Methodology
- Database for Fault Recording
- Raw Images
- Tiling
- Results and Discussions
- Transfer Learning - Image Classification
- Variations of Image Classification in Practice
- Creating Image Database
- Training
- Image Pre-Processing
- Implementation in Asset Management
- Current Asset Management System
- Adding AI to the Asset Management System
- Public Input
- Asset Inspector and Camera Network
- The New Model
- Conclusion
- References
- Chapter 9
- Applications of Non-Destructive Damage Evaluation and Structural Health Monitoring in Railway Track Maintenance
- Abstract
- Introduction
- Review on Main Technologies for CWR
- Main Technologies and Their Approaches for NDE and SHM
- The Magnetic Barkhausen Noise Method
- The Mechanical Method (VERSE and A-Frame)
- The Vibration Method (D'Stresen)
- Case Studies of NDE and SHM Technologies for CWR
- Application of The Magnetic Barkhausen Noise Method
- Application of The Mechanical Method (VERSE and A-Frame)
- Application of The Vibration Method (D'Stresen)
- Comparative Analysis of NDE and SHM Technologies
- The Magnetic Barkhausen Noise Method
- The Mechanical Method (VERSE and A-Frame)
- The D'Stresen or Vibration Method
- Conclusion and Recommendations on Future Work
- References
- Chapter 10
- Risk Management and Prioritisation of Rail Service Failures in Railway Track Maintenance
- Abstract
- Introduction
- Prevention of Rail Broken Using Ultrasonic Testing
- Ultrasonic Flaw Detectors
- Risks and Optimisation of the Ultrasonic Testing Work
- Concepts of the "Risk Ranking and Prioritisation Matrix"
- Raw Data and Pre-Processing for Data Analysis
- Methodologies and Technical Details for the Modelling
- Risk Ranking Based on Frequency (Month)
- Risk Ranking Based on Overdue Date
- Updated Risk Ranking by the Combination of Frequency and Overdue Date
- Risk Ranking Based on Rail Defects
- Risk Ranking Based on Critical Rail Defects
- Risk Ranking by the Combination of Rail Defects and Critical Defects
- Risk Ranking Based on the Condition of Rail Wear
- Prioritisation Matrix
- Remedial Actions
- Manual Work
- Technical Management of Surface Affected Rail
- Surface Affected Rail (SAR)
- Testability of Surface Affected Rail (SAR)
- Inspection Response Timeframe
- SAR Recurring Locations
- Assessment Requirements
- Stream 1 - Rail Defect History
- Stream 2 - Ultrasonic Testability History
- Stream 3 - Defect Specific Assessment
- Assessment Practices (Example for U1 and U2 RCF)
- Concept of Risk-Based Ultrasonic Rail Test Scheduling and Its Application
- Background and Objectives
- Project Description and Scope
- Data Analysis Model and Process
- Defect Initiation
- Defect Growth
- Detection Reliability
- Net Detection Efficiency
- Scheduling Guideline
- Analysis Model
- Definition of Parameters for a Specified Railway Line
- Risk Factors
- Case Study on North Coast Line
- Data Requirements
- Segmenting the North Coast Line
- Application to North Coast Line
- Results and Discussion
- Conclusions and Recommendations on Future Work
- References
- Chapter 11
- Digital Twin Approach for Lifecycle Management of Large-Scale Civil Infrastructure
- Abstract
- Introduction
- Digital Twin Approaches
- Definition of Digital Twins
- Buildings
- Bridges
- Discussion on Advantages, Challenges and Future Visions
- Advantages
- Design and Construction Phase
- Operation and Maintenance Phase
- Failure Prediction and Prevention Phase
- Existing Challenges
- Future Visions
- Conclusion
- References
- Editors' Contact Information
- Index
- Blank Page
- Blank Page
- Untitled
