Sensor Technologies for Civil Infrastructures
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Sensor Technologies for Civil Infrastructures

Volume 1: Sensing Hardware and Data Collection Methods for Performance Assessment

2nd Edition - July 19, 2022

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  • Editors: Jerome Lynch, Hoon Sohn, Ming Wang
  • eBook ISBN: 9780081026977

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Description

Sensor Technologies for Civil Infrastructure, Volume 1: Sensing Hardware and Data Collection Methods for Performance Assessment, Second Edition, provides an overview of sensor hardware and its use in data collection. The first chapters provide an introduction to sensing for structural performance assessment and health monitoring, and an overview of commonly used sensors and their data acquisition systems. Further chapters address different types of sensor including piezoelectric transducers, fiber optic sensors, acoustic emission sensors, and electromagnetic sensors, and the use of these sensors for assessing and monitoring civil infrastructures. The new edition now includes chapters on machine learning methods and reliability analysis for structural health monitoring. All chapters have been revised to include the latest advances in materials (such as piezoelectric and mechanoluminescent materials), technologies (such as LIDAR), and applications.

Key Features

  • Describes sensing hardware and data collection, covering a variety of sensors including LIDAR
  • Examines fiber optic systems, acoustic emission, piezoelectric sensors, electromagnetic sensors, terahertz technologies, ultrasonic methods, and radar and millimeter wave technology
  • Covers strain gauges, micro-electro-mechanical systems (MEMS), multifunctional materials and nanotechnology for sensing, and vision-based sensing and lasers
  • Includes new chapters on machine learning methods and reliability analysis

Readership

Structural and civil engineers, electronics engineers in academia and industry

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • List of contributors
  • Part One. Introduction and sensor technologies
  • 1. Introduction to sensors and sensing systems for civil infrastructure monitoring and asset management
  • 1.1. Introduction to infrastructure sensing
  • 1.2. Description of the book organization
  • 1.3. Summary
  • 2. Sensor data acquisition systems and architectures
  • 2.1. Scope of this chapter
  • 2.2. Concepts in signals and digital sampling
  • 2.3. Analog-to-digital conversion
  • 2.4. Digital-to-analog conversion
  • 2.5. Data acquisition systems
  • 2.6. Optical sensing DAQ system
  • 2.7. Wireless data acquisition
  • 2.8. Summary and future trends
  • 3. Commonly used sensors for civil infrastructures and their associated algorithms
  • 3.1. Introduction
  • 3.2. Brief review of commonly used sensing technologies
  • 3.3. Associated algorithms
  • 3.4. Examples of continuous monitoring systems
  • 3.5. Conclusions and future trends
  • 4. Piezoelectric transducers
  • 4.1. Introduction
  • 4.2. Principle of piezoelectricity
  • 4.3. Piezoelectric materials and the fabrication of piezoelectric transducers
  • 4.4. Piezoelectric transducers for SHM applications
  • 4.5. Bonding effects
  • 4.6. Limitations of piezoelectric transducers
  • 4.7. SHM techniques using piezoelectric transducers
  • 4.8. Applications of piezoelectric transducer–based SHM
  • 4.9. Future trends
  • 4.10. Chapter summary
  • 5. Optical fiber sensors
  • 5.1. Introduction
  • 5.2. Properties of optical fibers
  • 5.3. Common optical fiber sensors
  • 5.4. Future trends
  • 5.5. Sources for further advice
  • 5.6. Conclusions
  • 6. Acoustic emission sensors for assessing and monitoring civil infrastructures
  • 6.1. Introduction
  • 6.2. Fundamentals of acoustic emission technique
  • 6.3. Interpretation of AE signals
  • 6.4. AE localization methods
  • 6.5. Severity assessment
  • 6.6. AE equipment technology
  • 6.7. Field applications and structural health monitoring using AE
  • 6.8. Future challenges
  • 6.9. Conclusion
  • 7. Radar technology: radio frequency, interferometric, millimeter wave and terahertz sensors for assessing and monitoring civil infrastructures
  • 7.1. Introduction
  • 7.2. Radar and millimeter wave sensors
  • 7.3. Terahertz sensors
  • 7.4. Conclusions and future trends
  • 8. Electromagnetic sensors for assessing and monitoring civil infrastructures
  • 8.1. Introduction to magnetics and magnetic materials
  • 8.2. Introduction to magnetoelasticity
  • 8.3. Magnetic sensory technologies
  • 8.4. Role of microstructure in magnetization and magnetoelasticity
  • 8.5. Magnetoelastic stress sensors for tension monitoring of steel cables
  • 8.6. Temperature effects
  • 8.7. Eddy current
  • 8.8. Removable (portable) elastomagnetic stress sensor
  • 8.9. Conclusion and future trends
  • 9. Microelectromechanical systems for assessing and monitoring civil infrastructures
  • 9.1. Introduction
  • 9.2. Sensor materials and micromachining techniques
  • 9.3. Sensor characteristics
  • 9.4. MEMS sensors for SHM
  • 9.5. Application examples
  • 9.6. Durability of MEMS sensors for SHM
  • 9.7. Current research directions of MEMS sensors for SHM
  • 9.8. Further resources
  • 10. Laser-based sensing for assessing and monitoring civil infrastructures
  • 10.1. Laser-based sensing
  • Appendix
  • 11. Vision-based sensing for assessing and monitoring civil infrastructures
  • 11.1. Introduction
  • 11.2. Vision-based measurement techniques for civil engineering applications
  • 11.3. Important issues for vision-based measurement techniques
  • 11.4. Applications for vision-based sensing techniques
  • 11.5. Conclusions
  • 12. Introduction to wireless sensor networks for monitoring applications: principles, design, and selection
  • 12.1. Introduction and motivation
  • 12.2. Overview of wireless networks
  • 12.3. Hardware design and selection
  • 12.4. Wireless sensor network software
  • 12.5. Summary and outlook
  • 13. Vibration energy harvesters for sensing applications
  • 13.1. Introduction
  • 13.2. Harvester dynamic modeling
  • 13.3. Power availability and the optimal harvesting admittance
  • 13.4. Power extraction circuits
  • 13.5. Recent advancements and future directions
  • Part Two. Sensor data management
  • 14. Data management technologies for infrastructure monitoring
  • 14.1. Introduction
  • 14.2. An information modeling framework
  • 14.3. A scalable data management framework
  • 14.4. A cyberinfrastructure platform
  • 14.5. Utilization of the cloud-based cyberinfrastructure platform
  • 14.6. Summary and discussion
  • 15. Sensor data analysis, reduction, and fusion for assessing and monitoring civil infrastructures
  • 15.1. Introduction
  • 15.2. Bayesian inference and monitoring data analysis
  • 15.3. Data reduction
  • 15.4. Data fusion
  • 15.5. Further trends
  • 15.6. Sources of further information and advice
  • 16. Analytical techniques for damage detection and localization for assessing and monitoring civil infrastructures
  • 16.1. Introduction
  • 16.2. Linear time invariant systems
  • 16.3. Modal form
  • 16.4. Relation between the complex and the normal mode models
  • 16.5. Damage detection
  • 16.6. Damage localization
  • 16.7. Future trends
  • 16.8. Sources of further information and advice
  • 17. Output-only modal identification and structural damage detection using time–frequency and wavelet techniques for assessing and monitoring civil infrastructures
  • 17.1. Introduction
  • 17.2. Time–frequency methods: STFT, EMD, and HT
  • 17.3. Modal identification of linear time invariant and linear time variant systems using EMD/HT and STFT
  • 17.4. Modal identification of LTI and LTV systems using wavelets
  • 17.5. Experimental and numerical validation of modal identification of LTI and LTV systems using STFT, EMD, wavelets, and HT
  • 17.6. Conclusion
  • 18. Deep learning and data analytics for assessing seismic performance of civil infrastructures
  • 18.1. Introduction
  • 18.2. Stacked long short-term memory network
  • 18.3. Physics-guided convolutional neural network
  • 18.4. Physics-informed multi-LSTM networks
  • 18.5. Application examples
  • 18.6. Conclusions and future trends
  • 19. Prognosis and life-cycle assessment based on SHM information
  • 19.1. Introduction
  • 19.2. Statistical and probabilistic aspects for efficient prognosis
  • 19.3. Life-cycle analysis using monitoring data
  • 19.4. Multiobjective optimum monitoring planning
  • 19.5. Conclusions
  • Abbreviations
  • 20. Decision-making in SHM systems for asset management
  • 20.1. Introduction
  • 20.2. Bayesian inference and dynamic systems
  • 20.3. Optimal decision-making
  • 20.4. Value of information
  • 20.5. Examples
  • 20.6. Discussion and conclusions
  • Index

Product details

  • No. of pages: 676
  • Language: English
  • Copyright: © Woodhead Publishing 2022
  • Published: July 19, 2022
  • Imprint: Woodhead Publishing
  • eBook ISBN: 9780081026977

About the Editors

Jerome Lynch

Jerome P. Lynch is Associate Professor in the Department of Civil and Environmental Engineering at University of Michigan, USA.

Affiliations and Expertise

Ph.D., F.EMI, Vinik Dean of Engineering, Pratt School of Engineering, Duke University, Durham, NC, USA.

Hoon Sohn

Professor Hoon Sohn works at the Korea Advanced Institute of Science and Technology, Korea.

Affiliations and Expertise

Korea Advanced Institute of Science and Technology, Korea

Ming Wang

Distinguished Professor, Civil and Environmental Engineering, Northeastern University, USA.

Affiliations and Expertise

Northeastern University, USA

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